lacunary - Theorem Index

Theorem Index

This page lists all mathematical theorems, lemmas, and corollaries found across the site. Click on any item to jump to its location in the notes.

Found 171 theorem/lemma/corollarys
Theorem: CN is an inner product space \@{complex-inner-product-space}

The function

z,w=k=0N1zkwk

defined on z,wCN is an inner product, and so CN (N a positive integer) forms an @inner-product-space.

Proof \@{proof-of-complex-inner-product-space}

Since C is a field, CN is obviously a vector space.

To see that this is indeed an inner product, we'll show the required properties hold.

For @conjugate-symmetry, let zk=a+bi and let wk=c+di. Then,

zkwk=(a+bi)(cdi)=acadi+cbi+bd=(ac+bd)+(cbad)i,

and

wkzk=(c+di)(abi)=ac+adicbi+bd=(ac+bd)(adcb)i=(ac+bd)+(cbad)i,

and since k is arbitrary, it holds for any respective pair of components in z and w, and so z,w=w,z.

For @linearity in the first argument, let w,v,zCn,a,bC. Then

ax+by,z=(ax1+by1)z1+(ax2+by2)z2+=a(x1z1)+a(x2z2)++b(y1z1)+b(y2z2)+=ax,z+bx,z.

Now, to show @positive-definiteness let z=a+bi. Then,

z,z=zz=1>0.

Theorem: A sequence in Rk converges iff its components converge \@{sequence-in-rk-converges-iff-its-components-converge}

(a) Suppose xnRk,(n=1,2,3,) and xn=(α1,n,,αk,n).

Then, {xn} converges to x=(α1,,αk) if and only if

limnαj,n=αj,(1jk).

(b) Suppose {xn},{yn} are sequences in Rk, {βn} is a sequence of real numbers, and xnx,yny,βnβ. Then

limn(xn+yn)=x+y,limnxnyn=xy,limnβnxn=βx.

Proof \@{proof-of-sequence-in-rk-converges-iff-its-components-converge}

For (a), assume xnx. Then, from the definition of the norm,

|αj,nαj||xnx|,

that is, the distance from αk,n to αn is always less than or equal to the distance from xn to x. Therefore, for ϵ>0, |xnx|<ϵ|αj,nαj|<ϵ, and we can pick n to make this true for as small of ϵ as we'd like. Therefore, limnαj,n=αj.

Conversely, assume limnαj,n=αj. Let ϵ>0. For some integer N, when nN we have

|αj,nαj|ϵk,(1jk).

Therefore, nN implies that

|xnx|=j=1k|αj,nαj|2<ϵ,

so xnx.

Part (b) follows from part (a) and A sequence in Rk converges iff its components converge.

Theorem: A theorem about radius of convergence \@{theorem-9}

The radius of convergence of the Taylor series for a function f(z) about a point z0 is the distance from z0 to the nearest singularity of f(z).

If f has a power series expansion about a point z0 with nonzero radius of convergence, it must be the Taylor series about z0.

Theorem: Addition and Multiplication Rules \@{probability-rules}

The probability of A or B is the probability of A plus the probability of B minus the probability of A and B occurring together:

P(AB)=P(A or B)=P(A)+P(B)P(A and B)

If A and B are mutually exclusive events, P(AB)=P(A)+P(B).

We have to subtract the overlap between A and B to avoid double counting.

Similarly

P(AB)=P(A)+P(B)P(AB).

The probability of A and B is the probability of A times the probability of B given A, or equivalently, the probability of B times the probability of A given B.

P(AB)=P(A and B)=P(B and A)=P(A)P(B|A)=P(B)P(A|B)

If A and B are independent, this reduces to P(A)P(B).

Theorem \@{all-parameterizations-of-curve-have-same-line-integral}

When P(x,y) and Q(x,y) are continuous and C is smooth, we can calculate the value of the line integral by expressing P,Q,dx and dy in terms of any parametric representation of C and evaluating the resulting definite integral:

CP(x,y)dx+Q(x,y)dy=αβ{P[x(t),y(t)]dxdt+Q[x(t),y(t)]dydt}dt.(4.7)

All parameterizations of the curve lead to the same value.

Theorem \@{analytic-implies-cr-equations}

Assume a complex function f(z) is defined on a neighborhood of z0, and f is complex differentiable at z0. Let z=x+iy and write f(x+iy)=u(x,y)+iv(x,y). Then all of the partial derivatives ux,uy,vx,vy exist and

ux=vy,uy=vx,

and

f(x0+iy0)=ux(x0,y0)+ivx(x0,y0).

Proof \@{proof-of-analytic-implies-cr-equations}

By assumption and definition of the complex derivative, we have that

f(z0)=limΔz0f(z0+Δz)f(z0)Δz=limΔz0[u(x0+Δx,y0+Δy)u(x0,y0)]+i[v(x0+Δx,y0+Δy)v(x0,y0)]Δz.

Now, this limit must exist and be the same no matter what path we take. So, we can take the path along the real axis by setting Δy=0 and sending Δx0, and we get

f(z0)=f(x0+iy0)=limΔx0[u(x0+Δx,y0)u(x0,y0)]+i[v(x0+Δx,y0)v(x0,y0)]Δx,

and hence,

Ref(z0)=limΔx0u(x0+Δx,y0)u(x0,y0)Δx,

Imf(z0)=limΔx0v(x0+Δx,y0)v(x0,y0)Δx.

This gives us that the partial derivatives ux and vx exist and

ux(x0,y0)=Ref(x0+iy0),vx(x0,y0)=Imf(x0+iy0),

that is,

f(x0+iy0)=ux+ivx.(a)

Now, we can repeat this trick by sending Δz=Δx+iΔy to zero along the imaginary axis by setting Δx=0 and sending Δy0 to get

f(z0)=f(x0+iy0)=limΔy0[u(x0,y0+Δy)u(x0,y0)]+i[v(x0,y0+Δy)v(x0,y0)]iΔy=limΔy0[v(x0,y0+Δy)v(x0,y0)]i[u(x0,y0+Δy)u(x0,y0)]Δy,

and hence

Ref(z0)=limΔy0v(x0,y0+Δy)v(x0,y0)Δy,

Imf(z0)=limΔy0u(x0,y0+Δy)u(x0,y0)Δy.

This gives us that the partial derivatives uy and vy exist and

uy(x0,y0)=Imf(x0+iy0),vy(x0,y0)=Ref(x0+iy0),

that is,

f(x0+iy0)=vyiuy.(b)

Now, comparing (a) and (b) we have

ux+ivx=vyiuy,ux=vy,uy=vx.

Theorem \@{arc-length-in-plane}

If a smooth curve x=f(t),y=g(t),atb, is traversed exactly once as t increases from a to b, the curve's length is

L=abds=ab(dxdt)2+(dydt)2dt

The arc length of curve in R3 can be defined the same way, assuming z=h(t):

L=abds=ab(dxdt)2+(dydt)2+(dzdt)2dt

Theorem \@{balls-are-convex}

All balls are convex.

Proof \@{proof-of-balls-are-convex}

Let y,z be points in a ball with center x and radius r. Then, by definition, |yx|<r, and |zx|<r. Suppose p{λy+(1λ)z|0<λ<1}. We will show that |xp|<r.

|xp|=|x(λy+(1λ)z)|(substitute definition of p)=|xλyz+λz|(expand)=|xλyz+λz+λxλx|(add and subtract λx)=|λ(xy)+(1λ)(xz)|(factor λ and 1λ)λ|xy|+(1λ)|xz|(triangle inequality)<λr+(1λ)r(since |xy|,|xz|<r)=r(because λ+(1λ)=1).

So, p is within our ball and therefore all balls are convex.

Note \@{balls-are-convex-note}

Similar proofs can be used to show that closed balls and k-cells are also convex.

Theorem \@{binary-sequences-are-uncountable}

Let A be the set of all sequences whose elements are the digits 0 and 1. This set A is uncountable.

Proof \@{proof-of-binary-sequences-are-uncountable}

Let E be a countable subset of A, and call the elements of E s1,s2,s3,. We will construct a new sequence p in the following way:

pn=¬snn,n=1,2,3,

That is, the nth digit of p will be the opposite of whatever the nth digit of sn is. So, p differs from s1 in the first digit, from s2 in the second digit, s3 in the third digit, and so on, so that it differs from all elements of E, and therefore is not contained in E. But, p is definitely in A since it its elements are the digits 0 and 1. Therefore, E is a proper subset of A, so any countable subset of A must be a proper subset of A. But, A can't be a proper subset of itself, and therefore A must be uncountable.

Note \@{binary-sequences-are-uncountable-note}

This approach to proving this theorem is due to Cantor and is called diagonalization, and the animation below illustrates why.

Corollary \@{reals-are-uncountable}

The set of real numbers is uncountable.

Proof \@{proof-of-reals-are-uncountable}

I won't give a full proof here, but this can be accomplished by considering the binary representation of real numbers in the interval [0,1) consists of infinite sequences of 0 and 1.

Theorem: Binomial Series \@{binomial-series}

For complex z and complex α, we have

(1+z)α=n=0(αn)zn.

If |z|<1, the series converges absolutely for any any complex α, including negative integers.

Other convergence conditions are listed here.

Referenced by (1 direct)

Direct references:

Theorem: Binomial Theorem \@{binomial-theorem}

The expansion of any nonnegative integer power n of the @binomial x+y is a sum of the form

(x+y)n=k=0n(nk)xnkyk=k=0n(nk)xkynk.

Referenced by (2 direct)

Direct references:

Theorem: Bolzano-Weierstrass \@{theorem-27}

Every bounded sequence in Rk contains a convergent subsequence.

Proof \@{proof-of-theorem-27}

Note that any bounded sequence {pn}Rk is a subset of some closed set, bounded and thus compact k-cell in Rk. Therefore, {pn} is a sequence in a compact metric space, and has a convergent subsequence.

Theorem \@{cantor-bendixson-theorem}

Every closed set in a separable metric space is the union of a (possibly empty) perfect set and a set which is at most countable.

Proof \@{proof-of-cantor-bendixson-theorem}

Let X be a separable metric space and EX be closed. If E is at most countable, then we are done.

Suppose that E is uncountable. Note that the proof of Suppose ERk, with E uncountable,... only uses the property that Rk is a separable metric space, and it therefore generalizes to any separable metric space. Thus E is the union of a perfect set - its condensation points, P, and a set that is at most countable, EP.

Corollary \@{countable-closed-set-has-isolated-points}

Every countable closed set set in Rk has isolated points.

Proof \@{proof-of-countable-closed-set-has-isolated-points}

Let E be a countable closed set in Rk. Suppose for contradiction that E has no isolated points. Then every point of E is a limit point of E, and thus E is perfect set. But, every nonempty perfect set in Rk is uncountable, a contradiction. Thus, our assumption that E has no isolated points is incorrect, and E must contain isolated points.

Theorem \@{cantor-set-contains-no-segment}

The Cantor set contains no segment.

Proof \@{proof-of-cantor-set-contains-no-segment}

Suppose, for the sake of contradiction, that some segment (α,β)P and let L=βα. Pick some nN such that 1/3n<L. Now, En is the union of 2n intervals of length 1/3n, and since (α,β)P, it must be the case that (α,β) is a subset of some interval of length 1/3n. However, this can't be the case, since L>1/3n, by construction. Therefore, our provision assumption is incorrect, and P contains no segment.

Theorem \@{cantor-set-is-compact}

The Cantor set is compact.

Proof \@{proof-of-cantor-set-is-compact}

Clearly, P is bounded, for it lies within [0,1]. Each En is composed of the union of 2n closed intervals, and the union of finitely many closed intervals is also closed. P is then the intersection of infinitely many closed intervals, which is again closed. Therefore, P is closed and bounded, and by Heine-Borel, is compact.

Theorem \@{cantor-set-is-not-empty}

The Cantor set is not empty.

Proof \@{proof-of-cantor-set-is-not-empty}

Suppose En has [α,β] as an interval and thus α,βEn. Then, by definition, En+1 will contain [α,βα3] and 2(βα)3,β] as intervals, so α,βEn+1. Note that E0 has [0,1] as an interval. By induction, all En contain 0 and 1, and therefore so does their intersection P, and P is nonempty.

Referenced by (2 direct)
Theorem \@{cantor-set-is-perfect}

The Cantor set is a perfect set.

Proof \@{proof-of-cantor-set-is-perfect}

Let xP. Let r>0, and pick nN to be large enough that 1/3n<r. Then, x lies in one of the 2n intervals of length 1/3n in En; call it In. The endpoints of In are also in P (see The Cantor set is not empty.,) and at least one of them is not x. Since the endpoints are contained in a neighborhood of x with radius r>0, all neighborhoods of x are limit points of P, and therefore P is perfect.

Corollary \@{cantor-set-is-uncountable}

Because nonempty perfect sets in Rk are uncountable, and the Cantor set is nonempty and perfect, the Cantor set is uncountable.

Referenced by (1 direct)

Direct references:

Theorem: Cauchy criterion for convergence \@{cauchy-criterion-for-convergence}

A sequence converges in Rk if and only if it is a cauchy sequence.

Proof \@{proof-of-cauchy-criterion-for-convergence}

Suppose {pn}Rk converges. Then, because Rk is a metric space, {pn} is cauchy.

Conversely, suppose {pn}Rk is cauchy. Then, {pn} converges.

Theorem: Cauchy Integral Theorem \@{cauchy-integral-theorem}

If the derivative f of a complex function f is continuous in a domain containing a simple, closed, piecewise smooth curve C and its interior, then

cf(z)dz=0.

Theorem: Cauchy's Integral Theorem \@{cauchys-integral-theorem}

If f(z) is analytic in a @simply-connected domain D, then for every simple closed @path C in D,

Cf(z)dz=0.

Proof \@{proof-of-cauchys-integral-theorem}

Let f(z)=u(x,y)+i(v,y). By the definition of the contour integral, we have

Cf(z)dz=C(u+vi)(dx+dyi)=C(udxvdy)+iC(vdx+udy).

Because f is analytic, the Cauchy-Riemann Equations tell us that u(x,y) and v(x,y) have continuous first partial derivatives, and therefore, we can apply Green's Theorem. Now, we can take the first integral and transform it

C(udx+(vdy))=R(vx+uy)dxdy.

But, by Cauchy-Riemann Equations, vx=uy, so this integral evaluates to 0.

Now, for the second integral, using Green's Theorem again, we get

iC(vdx+udy)=iR(uxvy)dxdy,

and by Cauchy-Riemann Equations, ux=vy, so this integral also evaluates to 0, which makes the overall integral 0 as well.

Theorem: Cauchy-Goursat Theorem \@{cauchy-goursat-theorem}

If f is analytic inside and on a closed, piecewise smooth curve C, then

cf(z)dz=0.

Theorem: Cauchy-Riemann Equations \@{cauchy-riemann-equations}

A function f(z)=u(x,y)+v(x,y)i is analytic in an open set S if and only if the first partial derivatives of u(x,y) and v(x,y) are continuous on S and satisfy the Cauchy-Riemann equations therein

ux=vy,vx=uy.(c)

In polar form, we have z=r(cosθ+isinθ) and set f(z)=u(r,θ)+iv(r,θ),

so the condition is then

ur=1rvθ,vr=1ruθ.

Referenced by (1 direct)
Theorem: Central Limit Theorem \@{central-limit-theorem}

If c is a real constant and X1,X2, are independent random variables with mean μ and standard deviation σ, then

TODO: the precise statement is cut off in the source; complete it.

Theorem: Chain rule for joint entropy \@{joint-entropy-is-base-plus-conditional}

The entropy (joint entropy) of the joint event X,Y is the entropy of X plus the entropy of Y when X is known.

Proof \@{proof-of-joint-entropy-is-base-plus-conditional}

Note that

H(Y|X)=x,yp(x,y)logp(x,y)p(x)=x,yp(x,y)logp(x,y)+x,yp(x,y)logp(x).

The first term on the RHS is just x,yp(x,y)logp(x,y)=H(X,Y). The second is

x,yp(x,y)logp(x)=x[yp(x,y)]logp(x)=xp(x)logp(x)=H(X).

Altogether, we have that H(Y|X)=H(X,Y)H(X), or

H(X,Y)=H(X)+H(Y|X).

Referenced by (1 direct)

Direct references:

Theorem: Chebyshev's Inequality \@{chebyshevs-inequality}

Suppose t>0 and X is a random variable with the finite mean E[X] and standard deviation σ. Then

P(|XE[X]|t)σ2t2.

Note \@{chebyshevs-inequality-note}

Intuitively, this means that the probability that X is far away from its mean can't be too big.

Note that the upper-bound on this probability is proportional to the variance of the random variable and inversely proportional to the distance t of X from its mean. So, as the variance increases, the probability of X taking on values further from its mean also increases, and as the distance from the mean increases, the probability of X taking on values at least that far from E[X] decreases.

Also note that while Markov's Inequality only requires knowledge of the mean E[X] (the first moment), Chebyshev's inequality requires knowledge of the variance E[X2]E[X]2 (second moment). Also, Chebyshev's inequality works for any random variable with a variance defined, rather than just on non-negative random variables.

Proof \@{proof-of-chebyshevs-inequality}

Let X be a random variable with mean E[X] and variance σ2. Define the non-negative random variable

Y=(XE[X])2.

By Markov's Inequality, for any a>0, we have

P(Ya)E[Y]a.

Set a=t2. Then,

P((XE[X])2t2)E[(XE[X])2]t2.

Since the variance of X is σ2=E[(XE[X])2], it follows that

P((XE[X])2t2)σ2t2.

Note that

(XE[X])2t2|XE[X]|t.

Thus,

P(|XE[X]|t)σ2t2.

Referenced by (2 direct)
Theorem \@{closed-orbits-impossible-in-gradient-systems}

Closed orbits are impossible in gradient systems.

Intuition \@{closed-orbits-impossible-in-gradient-systems-intuition}

If we're always moving "downhill" in some direction in a space, it's impossible to come back to where we started. This is another reason why oscillations aren't possible in one dimensional systems.

Theorem \@{closed-path-of-path-independent-integral-is-zero}

If a line integral is independent of path in a domain D, and C is a closed, piecewise smooth curve in D that contains only points of D in its interior, its value is zero:

CPdx+Qdy=0

Theorem \@{closed-subsets-of-compact-sets-are-compact}

Closed subsets of compact sets are compact.

Proof \@{proof-of-closed-subsets-of-compact-sets-are-compact}

Suppose FKX, with F closed relative to X, and K compact. Let {Vα} be an open cover of F. Since Fc is open relative to X (see A set E is open iff its...), if we add it to {Vα}, we obtain an open cover of K; let's call it Ω. Since K is compact, we can obtain a finite subcover of K by discarding all but a finite number of sets from Ω; let's call it Φ. Since FK, Φ is also a finite subcover of F, and therefore F is compact.

Note \@{closed-subsets-of-compact-sets-are-compact-note}

If FcΦ, we may, but aren't required, to exclude it, and still have a finite open cover of F.

Corollary \@{intersection-of-closed-and-compact-is-compact}

If F is closed and K is compact, then FK is compact.

Proof \@{proof-of-intersection-of-closed-and-compact-is-compact}
Theorem \@{closure-distributes-over-finite-unions}

Closure distributes over finite unions.

If Bn=i=inAi, then Bn=i=1nAi,n=1,2,3,.

Proof \@{proof-of-closure-distributes-over-finite-unions}

Let pi=1nAi. Then, pAi for some i. If pAi, then pi=inAi=Bn, so pBn. If p is in some Ai, then every neighborhood of p contains some point qAi,qp, and since AiBn, qBn, so pBn.

Conversely, let pBn. If pBn, pAi for some i, and thus pAii=1nAi. If p is only in Bn, suppose for contradiction that pi=1nAi. For each i, let Ni be a neighborhood centered at p with NiAi=. Then, N=i=1nNi is a neighborhood of p (see (a) - For any collection {Ga} of...,) but NNi= for all i, so NBn=. But, this contradicts our hypothesis that pBn, so our contradictory assumption must be invalid, and pi=1nAi.

Theorem \@{compact-implies-closed}

Any compact subset K of a metric space X is closed.

Proof \@{proof-of-compact-implies-closed}

Suppose K is compact relative to metric space X. Let pKc. For each qK, we can define rq=12d(p,q), and let Pq and Qq be neighborhoods of radius rq around p and q, respectively. Note that Pq and Qq are disjoint, because we defined their radii to be half the distance between them, and they are open.) Now, since K is compact, we can pick a finite number of points in K, q1,,qn, such that KQq1Qqn=Q (Q is a finite subcover of K.) Using the same set of points as reference, let Pq1Pqn=P. Note that PQ={}, since each Pq is disjoint with its paired Qq (to be in P, a point must be in all Pq, but any point in Q is not in at least one Pq.) P is open, since it is the intersection of finitely many open sets (see (a) - For any collection {Ga} of...) and obviously contains p, since each Pq contains p. Therefore, p has a neigborhood P that is disjoint with K (since PKPQ={}), and is therefore an interior point of Kc. It follows that Kc is open, and that K is closed.

Theorem \@{compact-metric-space-has-countable-base}

Every compact metric space has a countable base and is therefore separable.

Proof \@{proof-of-compact-metric-space-has-countable-base}

Let K be a compact metric space. Fix nN (any natural number will do,) then, consider the open cover {Gα}n of balls of radius 1/n centered at xK. Now, because K is compact, some finite {Vα}n{Gα}n covers K.

Now, consider the set of all balls for all n,

C=n=1{Vα}n

Because the union of a sequence of countable sets is countable, C is countable. To show C is a base for K, let G be an open subset of K, and let xG. Let ϵ>0 such that Bϵ(x)G. Pick natural n such that 1/n<ϵ/2. Then, x{Vα}n for some α, (because no point in K is more than 1/n away from the center of some {Vα}n and therefore C is a countable base of K. Since we can make ϵ as close to 0 as we like, the centers of {Vα}n are dense as well as countable, and are therefore a countable dense subset of K, so K is separable.

Theorem \@{compact-metric-spaces-are-complete}

Compact metric spaces are complete.

That is, if X is a compact metric space and if {pn} is a cauchy sequence in X, then {pn} converges to some point of X.

Proof \@{proof-of-compact-metric-spaces-are-complete}

Let {pn} be a cauchy sequence in the compact metric space X. For N=1,2,3,, let En bet the set consisting of pN,pN+1,pN+2,. Then

limNdiamEN=0,

by two theorems above. Each En is a closed subset of the compact space X, and is thus compact. Also, EN+1EN, which implies that EN+1EN. Now, we have that there is a unique pX which lies in every En.

Let ϵ>0. Since limNdiamEN=0, there is an integer N0 such that diamEn<ϵ if NN0. Since pEn, we have that d(p,q)<ϵ for every qEN and thus for every qEn. That is, d(p,pn)<ϵ if nN0, so {pn} converges to p.

Referenced by (1 direct)
Referenced by (2 direct)
Theorem \@{compact-relative-to-subspace}

Suppose KYX. Then K is compact relative to X iff K is compact relative to Y.

Proof \@{proof-of-compact-relative-to-subspace}

Suppose K is compact relative to X and that {Vα} is an open cover of K relative to Y, such that KαVα. We need to show that a finite subset of {Vα} covers K. Because {Vα} is open relative to Y and YX (see Suppose YX. A subset E...) there are sets Gα, open relative to X, such that Va=YGα, for each α. Now, since K is compact relative to X, we have KGα1Gαn,(a) for some finite set of indices a1,,an. Now, since KY, KVα1Vαn,(b) which shows K is compact relative to Y.

Conversely, suppose K is compact relative to Y and let {Gα} be a cover of K open relative to X. We need to show there is a finite subset of {Gα} that covers K. Let Vα=YGα, for each α. Then (b) will hold for some set of indicies, α1,,αn, and since each VαGα, (a) is implied by (b) and we've shown K is compact relative to X.

Theorem \@{complement-of-intersection-is-union-of-complements}

The complement of an intersection is equal to the union of complements.

Proof \@{proof-of-complement-of-intersection-is-union-of-complements}

Let A and B be sets. We want to show that

(AB)c=AcBc.

Suppose x(AB)c. Then, x is not in AB, that is, x is either not in A or it is not in B or it is in neither. If x is not in A, then it is in Ac, and therefore it is in AcBc. The same approach works with B, and therefore xAcBc, and we have shown (AB)c=AcBc.

Theorem \@{complement-of-union-is-intersection-of-complements}

The complement of a union is equal to the intersection of complements.

Proof \@{proof-of-complement-of-union-is-intersection-of-complements}

Let A and B be sets. We want to show that

(AB)c=AcBc.

Suppose x(AB)c. Then, if xA or xB, then xAB and x(AB)c, a contradiction. Therefore, xA and xB. That is, xAc and xBc, therefore xAcBc.

Theorem \@{complement-of-union-is-intersection-of-complements-general}

Let Eα be a collection of sets. Then

(αEα)c=α(Eαc).

Proof \@{proof-of-complement-of-union-is-intersection-of-complements-general}

Suppose x(αEα)c. Then, xαEα, so x is not in any Eα. Therefore, for every Eα, xEαc, and thus xα(Eαc). Conversely, suppose xα(Eαc). Then, x is in every Eαc, that is, x is not in any Eα. Therefore, x(αEα)c.

Note \@{complement-of-union-is-intersection-of-complements-general-note}

This is just De Morgan's law extended to arbitrary indexed collections.

Theorem \@{composition-of-continuous-functions-is-continuous}

Suppose X,Y,Z are metric spaces, EX, f:EY, g:f(E)Z, h:EZ with

h(x)=g(f(x))(xE).

If f is continuous at a point pE and if g is continuous at the point f(p), then h is continuous at p.

Proof \@{proof-of-composition-of-continuous-functions-is-continuous}

Let ϵ>0. Since g is continuous at f(p), there exists η>0 such that

dZ(g(y),g(f(p)))<ϵ if dY(y,f(p))<η and yf(E).

Since f is continuous at p, there exists δ>0 such that

dY(f(x),f(p))<η if dX(x,p)<δ and xE.

It follows that

dZ(h(x),h(p))=dz(g(f(x)),g(f(p)))<ϵ

if dX(x,p)<δ and xE. Thus, h is continuous at p.

Intuition \@{composition-of-continuous-functions-is-continuous-intuition}

Basically, since g is continuous, we can control how close its output is to g(f(p)) by controlling how close its input is to f(p), which we can certainly do, since f is also continuous, and we can control how close its output is to f(p) by controlling how close its input is to p.

Theorem \@{condensation-points-of-an-uncountable-subset-of-rk-are-perfect}

Suppose ERk, with E uncountable, and let P be the set of all condensation points of E. Prove that P is perfect and that at most countably many points of E are not in P, that is, that PcE is at most countable.

Proof \@{proof-of-condensation-points-of-an-uncountable-subset-of-rk-are-perfect}

Let {Vn} be a countable base of Rk (see Rk is separable. and Every separable metric space has a countable...,) and let W be the union of those Vn for which EVn is at most countable. We will show that P=Wc.

Suppose pWc. Then p is in no Vn for which VnE is at most countable, that is, every neighborhood of p has uncountably many points in E, and thus pP.

Conversely, suppose pP. Suppose, for the sake of contradiction, that pW. Then pVn for some Vn where VnE is at most countable. But, since p is an interior point of this Vn, there is a neighborhood N(p)Vn, and since every neighborhood of p has uncountably many points in E, we have a contradiction, and thus our assumption that pW must be incorrect, and therefore pWc, and P=Wc. Furthermore, since W is a union of open sets, W is open, and Wc=P is closed.

Since W is open, only countably many Vn are required to cover it. Each of these Vn has at most countably many points in E, so W=Pc has at most countably many points in E, that is, there are at most countably many points of E that are not in P.

Now, to show all points in P are limit points of P, suppose pP. Let Nr(p),r>0 be a neighborhood of p. Then, Nr(p)E is uncountable. Now, since there are at must countably many points in E that are not in P, there are at most countably many points in (Nr(p)E)P, and therefore there must be uncountably many points in Nr(p)EP. Therefore, every neighborhood of p contains infinitely many points in P other than p, p is a limit point of P, and P is perfect.

Referenced by (1 direct)
Theorem: Confidence Interval for a Mean, Known Variance \@{ci-mean-known-variance}

If x¯ is the mean of a random sample of size n from a population with a known variance σ2, a 100(1α)% confidence interval for μ is given by

x¯zα/2σn<μ<x¯+zα/2σn.

Theorem: Confidence Interval for a Mean, Unknown Variance \@{ci-mean-unknown-variance}

If x¯ is the mean of a random sample of size n from a population with an unknown variance, and the standard deviation of the sample is s, a 100(1α)% confidence interval for μ is given by

x¯tα/2sn<μ<x¯+tα/2sn.

Theorem \@{connected-sets-in-r1-are-intervals}

A subset E of the real line R1 is connected if and only if it has the following property: If xE,yE, and x<z<y, then zE.

Proof \@{proof-of-connected-sets-in-r1-are-intervals}

We will proceed both sides of the implication by proving the contrapositive, i.e., that if the interval property doesn't hold, then the set isn't connected, and conversely, that if the set isn't connected, the interval property doesn't hold.

Suppose x,yE and z(x,y),zE. Then E=AzBz, where

Az=E(,z),Bz=E(z,).

Since xAz and yBz, they are nonempty, and since Az(,z) and Bz(z,), they are separated. Therefore, E is not connected.

Conversely, suppose, for the sake of contradiction, that E is not connected. Then there are nonempty separated sets A an B such that AB=E. Let xA,yB and assume x<y. Define

z=sup(A[x,y]).

By Let E be a nonempty set of..., zA, and because A and B are separated, zB. Therefore xz<y.

If zA, it follows that x<z<y, and zE.

If zA, then zB, hence there exists z1 such that z<z1<y and z1B (because zB means there is a neighborhood of z that contains no points of B.) Thus, x<z1<y and z1E.

Theorem: Conservative Sample Size for Estimating a Proportion \@{sample-size-proportion-conservative}

If p^ is used as an estimate of p, we can be at least 100(1α)% confident that the error will not exceed a specified amount e when the sample size is

TODO: the sample-size formula is missing from the source; complete this statement.

Theorem \@{conservative-systems-have-no-attracting-fixed-points}

Conservative systems have no attracting fixed points.

Proof \@{proof-of-conservative-systems-have-no-attracting-fixed-points}

Suppose x were an attracting fixed point in a conservative system. Then, note that all trajectories in the basin of attraction approach x as t. Since E(x) is continuous, the energy in the limit as trajectories approach x is equal to the energy at the fixed point, and so the energy along the entirety of each trajectory is equal to the energy at the fixed point. But, this implies that the energy in the entire basin of attraction is the same as the energy at the fixed point, which violates our definition of a conservative system (i.e. we require that E(x) be nonconstant.) Therefore no such fixed point can exist.

Theorem: Continuity Theorem for Moment Generating Functions \@{mgf-continuity-theorem}

Let {Xn} be a sequence of random variables with moment generating functions defined by

MXn(t)=E[etXn]

for all t<ϵ for some ϵ>0.

Suppose there exists a function M(t), finite for t<ϵ, such that

limnMXn(t)=M(t)

for all t<ϵ, and that M(t) is the moment generating function of some random variable X.

Then the distributions of {Xn} converge in distribution to X, i.e.,

XndX.

Theorem \@{convergent-sequences-are-bounded}

If {pn} converges, then {pn} is bounded (sequence).

Proof \@{proof-of-convergent-sequences-are-bounded}

Let ϵ>0. Only finitely many points in {pn} lie outside of Bϵ(p). That is, for some integer N, only the points pn where nN lie outside of Bϵ(p). Let δ=max{ϵ,d(p,p1),d(p,p2),,d(p,pn)},n=1,2,,N. Then, d(p,pn)<δ for all n=1,2,3,.

Theorem \@{cos-characterization-of-dot-product}

The dot product of u and v is

uv=|u||v|cosθ

where θ is the angle between u and v.

Referenced by (1 direct)

Direct references:

Theorem \@{cyclic-subgroup-generated-by-powers}

Let G=a be a cyclic group with n elements. Let bG and b=as. Then b generates a cyclic subgroup of G containing nd elements, where d=gcd(s,n). Two cyclic subgroups as and at are equal if and only if gcd(s,n)=gcd(t,n).

Proof \@{proof-of-cyclic-subgroup-generated-by-powers}

Suppose gG with ord(g)=n and let mN. Then, gm=ggcd(m,n).

So, g=gmgcd(m,n)=1.

from Groups
Theorem: de Moivre's Theorem \@{de-moivres-}

zn=rn(cosnθ+isinnθ)

Theorem \@{diameter-of-set-equals-diameter-of-closure}

If E is the closure of a set E in a metric space X, then

diamE=diamE.

Proof \@{proof-of-diameter-of-set-equals-diameter-of-closure}

Because EE, diamEdiamE.

Conversely, Let p,qE, and p,qE, such that d(p,p)<ϵ,d(q,q)<ϵ. Therefore, by the triangle inequality,

d(p,q)d(p,p)+d(p,q)+d(q,q)<2ϵ+d(p,q)2ϵ+diamE.

Therefore, diamE2ϵ+diamE, and since ϵ was arbitrary, diamE=diamE.

Referenced by (1 direct, 1 transitive)
Theorem \@{directional-derivative-is-inner-product-of-vector-and-grad}

The directional derivative of f in the direction of a unit vector u is the inner product of u and gradf, that is,

Du=ugradf.

Referenced by (1 direct)

Direct references:

Theorem: Divergence Theorem of Gauss \@{divergence-theorem}

Let T be a closed bounded region in a space whose boundary is a @piecewise smooth @orientable surface S. Let F(x,y,z) be a vector function that is continuous and has continuous first partial derivatives in some domain containing T. Then

TdivFdV=SFndA.

Theorem: Divergence Theorem, or, Gauss's Theorem \@{theorem-33}

An important theorem related to divergence is the Divergence Theorem (also known as Gauss's theorem) which connects the flux of a vector field through a closed surface to the divergence of the field inside the volume bounded by the surface:

V(F)dV=sFdS

Theorem: Doubly stochastic maps increase entropy \@{theorem-29}

Take a @doubly-stochastic @matrix A and @probability-vector p, let q=Ap. Then H(q)H(p), with equality iff q is a @rearrangement of p.

Proof \@{proof-of-theorem-29}

Note that qi=jAijpj, by the definition of vector matrix multiplication. Now we'll define a couple of joint distributions:

rij=Aijpj,sij=Aijqi.

Now we find the relative-entropy from s to r:

D(r||s)=i,jrijlogrijsij=i,jAijpjlogAijpjAijqi=i,jAijpjlogpjqi=i,jAijpj(logpjlogqi)=i,jAijpjlogpji,jAijpjlogqi

Now, addressing the first term on the RHS of the last line:

i,jAijpjlogpj=j[pjlogpjiAij]=j[pjlogpj1]=jpjlogpj=H(p).

And the second term on the RHS of the last line:

i,jAijpjlogqi=i[logqijAijpj]=i[logqiqi]=H(q).

So, we end up with D(r||s)=H(q)H(p). Since D(r||s)0 by Gibbs' Inequality, we have H(q)H(p)0, which implies that H(q)H(p), which is what we wanted to show.

Now, the equivalence case. If q is just a rearrangement of p, then obviously it has the same entropy as we can just re-index to recover p. Now assume H(p)=H(q). Then, H(q)H(p)=0D(r||s)=0r=sAijpj=Aijqipj=qi whenever Aij>0. Now, for each value c appearing in p or q, let

Jc={j:pj=c},Ic={i:qi=c},

that is Jc is the set of indices of p where pj=c, and similarly with Ic and q. Now, consider row iIc. For any Aij>0, we have pj=qi, so jJc, i.e. every nonzero entry of row i lies in a column of Jc, so the columns jJc have the entire mass of row i. Similarly, for column jJc, for nonzero Aij, qi=pj=c, so iIc, and the rows Ic contain all of column j's mass. Taking 1S as the @indicator-vector of a set S, we have that

A1Jc=1Ic,

that is, rows in Ic sum to 1 over the columns Jc, while rows outside Ic have no mass in the columns Jc at all. Now we count the mass:

|Ic|=11Iccount entries via dot product=1(A1Jc)identity from above=(1A)1Jcassociativity=11Jccolumn-stochasticity=|Jc|count entries via dot product.

Therefore, |Ic|=|Jc| for all c, so each c appears equally often in p and in q, and q is therefore a rearrangement of p.

Intuition \@{theorem-29-intuition}

Averaging (well, replacing each probability with a convex combination of all the probabilities in a way that retains their summing to 1) the probabilities in p can only bring them closer to each other (if it doesn't just rearrange them), which increases entropy.

Theorem \@{euclidean-space-is-metric-space}

(Rn,d:RnR=|xy|) is a metric space for any n0.

Proof \@{proof-of-euclidean-space-is-metric-space}

First, for n=0, R0 is just the empty set, so the metric axioms are vacuously satisfied for all points in the set. Now, for n1,

  • Let p,qRn,pq. Then |pq|<0, so d(p,q)>0.
  • Let pRn. Now, pp=0, so |pp|=0, so d(p,p)=0.
  • Let p,qRn. |pq|=|qp|, so d(p,q)=d(q,p).
  • Let p,q,rRn. |pq||pr|+|rp|, so d(p,q)d(p,r)+d(r,p).

Therefore, d is a metric on Rn and (Rn,d) is a metric space.

Theorem \@{euclidean-space-is-separable}

Rk is separable.

Proof \@{proof-of-euclidean-space-is-separable}

The points of Rk that have only rational coordinates are a subset of Rk, we'll call it Qk, countable and dense.

We know that the rationals are countable, and because n-tuples of countable elements are countable, Qk is countable.

To show Qk is dense, consider an arbitrary point p in Rk. Now, let ϵ>0. Since the the rationals are dense in the reals, we can pick a qQk with d(p,q)<ϵ, by picking rational approximations of the coordinates of p and forming q such that q is within ϵ of p, i.e.

|piqi|<ϵk|pq|<i=1k(piqi)2<kϵk=ϵ.

Theorem \@{euclidean-spaces-are-complete}

All Euclidean spaces are complete

That is, in Rk, every cauchy sequence converges.

Proof \@{proof-of-euclidean-spaces-are-complete}

Let {xn} be a cauchy sequence in Rk. Define EN as in the proof above, but with xi in place of pi. For some N,diamEn<1. The range (sequence) of {xn} is the union of En and the finite set {x1,,xN1}. Hence, {xn} is bounded (sequence) (since the finite set of points can be contained in some bounding box, and the remaining points can be contained in some ball with diameter 1.) Since {xn} is bounded, it is compact, and thus {xn} is a subset of a compact metric space (its closure), and so converges.

Referenced by (1 direct)
Theorem \@{every-convergent-sequence-in-a-metric-space-is-a-cauchy-sequence}

Every convergent sequence in a metric space X is a Cauchy sequence.

Proof \@{proof-of-every-convergent-sequence-in-a-metric-space-is-a-cauchy-sequence}

Suppose {pn} is a convergent sequence in a metric space X. Let ϵ>0. Then for some N,d(p,pn)<ϵ when nN. Thus,

d(pn,pm)d(p,pn)+d(p,pm)<2ϵ

whenever n,mN, and so {pn} is Cauchy.

Referenced by (1 direct)
Theorem \@{every-k-cell-is-compact}

Every k-cell is compact.

Proof \@{proof-of-every-k-cell-is-compact}

Let I be a k-cell, consisting of all points x=(x1,,xk) such that ajxjbj,1jk. Let δ=j=1k(bjaj)2, i.e., the maximum distance between any two points in I (the diagonal). Then for any points x,yI,|xy|δ.

Suppose, for the sake of contradiction, that there is an open cover {Gα} of I that contains no finite subcover of I. Now, let cj=(aj+bj)/2, i.e. cj is the midpoint of [aj,bj]. We can subdivide I into 2k k-cells Qi, determined by the intervals [aj,cj] and [cj,bj]. At least one Qi, call it I1, cannot be covered by any finite subcollection of {Gα}, or else I would have a finite subcover in {Gα}. We then can subdivide I1 and so on, obtaining a sequence {In} with the following properties:

(a) In+1In (each k-cell in the sequence is nested in the previous.)

(b) In is not covered by any finite subset of {Gα}.

(c) If x,yIn, then |xy|2nδ.

From (a) and If In is a sequence of intervals..., there is some point x that is in every In. For some α,xGα, since {Gα} is a cover of all of I. Gα is open, so for some r>0,|yx|<r implies that yGα, that is, x has a neighborhood that lies entirely within Gα. If we make n big enough, we have that 2nδ<r, so by (c), InGα, that is, In is entirely covered by Gα. But, this contradicts (b), so our provisional assumption is incorrect, and {Gα} must have a finite subcover that covers I, and therefore I is compact.

Intuition \@{every-k-cell-is-compact-intuition}

If a k-cell has an open cover {Gα}, then any point in it will be in some Gα, and can therefore be surrounded by an open ball with some positive radius, lying entirely in Gα. That open ball takes up some space, and we can then subdivide the k-cell into small enough parts that some part is entirely within that open ball. We still have finitely many subdivisions, and each of those could be covered with a similary constructed open ball, which means we can cover the entire k-cell with finitely many open balls covered by finitely many elements of {Gα}.

Referenced by (2 direct)
Theorem \@{every-metric-space-where-every-infinite-subset-has-a-limit-point-is-separable}

Let X be a metric space in which every infinite subset has a limit point. Then X is separable.

Proof \@{proof-of-every-metric-space-where-every-infinite-subset-has-a-limit-point-is-separable}

Let δ>0, and pick x1X. Now, continue picking xj+1 such that d(xi,xj+1)δ i.e. so that each new point is at least δ away from each existing point. Suppose this process continues infinitely; then the points {xi} are an infinite subset of X, and thus must have a limit point p. Now, because every neighborhood of p must contain infinitely many points in xi, we can let r=δ/2, Nrp be a neighborhood of p, and pick x,y{xi},Nrp. But, by the triangle inequality

d(x,y)d(x,p)+d(p,y)<δ/2+δ/2=δ

so d(x,y)<ϵ, which contradicts our assumption that {xi} could be an infinite set where all points were at least δ apart. Therefore, {xi} has only finitely many points, and X can be covered with finitely many open balls of radius δ (for if it couldn't be, we could always fit another point into {xi}.)

Now, if we let δ=1/n,n=1,2,3,, we can consider the set of points {xi}n as the finite set of points at least 1/n apart in X. The collection of all such points can be called {xin} and is dense in X: let p be a point in X. and Nrp,r>0 a neighborhood of p. If we pick n such that 1/n<r, then some {xi}n will be in Nrp, because if not, we would have a contradiction with the fact that shown above that no point in X is more than 1/n away from a point in {xi}n.

Since each {xi}n is finite, and there are countably many {xi}n, the {xin} is a countably dense subset of X and therefore X is separable.

Referenced by (1 direct)
Theorem \@{every-neighborhood-is-an-open-set}

Every neighborhood is an open set.

Proof \@{proof-of-every-neighborhood-is-an-open-set}

Suppose Nr(p) is a neighborhood in X. Let qNr(p). We need to show that q is an interior point of Nr(p). Let s=rd(p,q); because d(p,q)<r, we have s>0. Now let Ns(q) be the neighborhood of radius s around q. We need to show that Ns(q)Nr(p). Suppose xNs(q). First note that because s=rd(p,q), d(p,q)=rs. Now,

d(p,x)d(p,q)+d(q,x)<rs+s=r.

Therefore, Ns(q)Nr(p), so q is an interior point of Nr(p), and since q was arbitrary, every point of Nr(p) is interior. Hence, Nr(p) is open.

Referenced by (1 direct)
Theorem \@{every-separable-metric-space-has-a-countable-base}

Every separable metric space has a countable base.

Proof \@{proof-of-every-separable-metric-space-has-a-countable-base}

Let X be a separable metric space, and let xGX, with G @open-set. Now, because X is separable, it by definition has a countable dense subset E. If xE, we can pick a rational δ>0 and let Nδ(x) be a neighborhood such that Nδ(x)G (δ must also be small enough such that this neighborhood is within G, which is possible because x is an interior point of G and we can pick a rational as close to any real as we'd like.) Now we have that xNδ(x)G, and since there are countably many xE and countably many neighborhoods with rational radius around each xE, there are countably many such neighborhoods in G. On the other hand, if xE, then x is a limit point of E, and thus there is pE as close as we'd like to x. Pick pE such that d(p,x)<δ for some rational δ such that xNδ(p)G. Again, since there are countably many such pE, with countably many neighborhoods of rational radius each, there are countably many such neighborhoods in G.

Now, if we let G be the union of all open sets GβX, then G is open, and let Vα be the union of all the Nr(q),qGE, with rQ, then every xG is in some Vα, and there are countably many Vα, so Vα is a countable base for X.

Theorem \@{existence-and-uniqueness-of-ivp-solutions}

Consider the @initial-value-problem

x˙=f(x),x(0)=x0.

Suppose that f(x) and f(x) are continuous on an open interval R of the x-axis, and suppose that x0 is a point in R. Then, the initial value problem has a solution x(t) on some time interval (τ,τ) about t=0, and the solution is unique.

Theorem \@{find-tangent-plane}

If S is given by F(x,y,z)=0, then at P=(x0,y0,z0), the tangent plane is

F(P)xx0,yy0,zz0=0.

Theorem: Finite Geometric Series \@{finite-geometric-series}

Let wC and N be a positive integer. Then

k=0N1wk=1wN1w.

Proof \@{proof-of-finite-geometric-series}

First, define SN as

SN=k=0N1wk=1+w+w2++wN1.

Then

SN=1+w+w2++wN1wSN=w+w2+w3++wN

Subtracting the second equation from the first gives

SNwSN=(1+w+w2++wN1)(w+w2+w3++wN)=1wN,

because everything in the middle cancels out.

Now, we can factor out 1w from the left hand side to get

(1w)SN=1wNSN=1wN1wk=0N1wk=1wN1w.

Referenced by (1 direct)

Direct references:

Theorem \@{finite-sets-are-compact}

Every finite set is compact.

Proof \@{proof-of-finite-sets-are-compact}

Suppose K is a finite set in metric space X and that {Gα},αA is an open cover of K. Since K is finite, we can enumerate its points as {k1,,kn}, for some n0. Then, for each i=1,,n (there are none when n=0,) pick an α(i)A with xiGα(i). Define the index set A0={α(1),,α(n)}A. Because n is finite, A0 is finite as well, and K={k1,,kn}αA0Gα, so {Gα},αA0 is a finite sub-cover of the original open cover. Therefore, every open cover of K has a finite sub-cover, and K is compact.

Theorem: First Simple Pole Residue Formula \@{first-simple-pole-residue-formula}

Resz=z0f(z)=b1=limzz0(zz0)f(z).

Theorem: Fourier Basis \@{fourier-basis}

Let N be a positive integer. Then the vectors

ϕn=(e2πink/N)k=0N1

for n=0,,N1 form a basis for CN.

Proof \@{proof-of-fourier-basis}

To show linear independence, we will show a stronger condition, orthogonality, holds.

For m,n,kZN,mn we can write that the kth entry of ϕm is

ϕm(k)=e2πiN(km(modN)),

and that the conjugate of the kth entry of ϕn is

ϕn(k)=e2πiN(kn(modN)).

Then, in our inner product between ϕm and ϕn, the kth term is

ϕm(k)ϕn(k)=e2πiN(km(modN))e2πiN(kn(modN))=e2πiN(k(mn)(modN)).

Thus, for r=mn(modN),

ϕm,ϕn=k=0N1e2πiNkr.

Now we need to show that this sum is 0. Note that if we let w=e2πiNr, we can rewrite this as

ϕm,ϕn=k=0N1wk.

From Finite Geometric Series, we have that

k=0N1wk=1wN1w.

Substituting back in e2πiNr for w we get

k=0N1e2πiNrk=1e2πiNrN1e2πiNr=11r1e2πiNr=0.

Thus, ϕm,ϕn=0, and so when mn, ϕm an ϕn are orthogonal and therefore @linearly-independent.

Now we need to show that ϕ0ϕN1 span CN.

Let u=(z0,z1,,zN1)CN. We need to find c0,c1,,cN1 such that

u=c0ϕ0+c1ϕ1++cN1ϕN1.

That is, we want constants c0cN1 such that

uu=n=0N1cnϕn.

To find some specific cm, we can take the inner product of both sides with respect to ϕm to get

u,ϕm=n=0N1cnϕn,ϕm.

@Linearity in the first argument of the inner product allows us to rewrite the right side to get

u,ϕm=n=0N1cnϕn,ϕm.

Now, we know that

ϕn,ϕm={N,n=m,0,nm.

so the inner product on the right-hand side is 0 except when n=m, and this reduces to

u,ϕm=cmN.

Therefore,

cm=u,ϕmN.

This shows that the Fourier basis described here indeed spans CN, and shows how to compute the Fourier coefficients c0cN1.

Theorem \@{function-is-continuous-at-point-iff-limit-at-point-equals-function-at-point}

Suppose X and Y are metric spaces, EX,pE, with p a limit point of E, and f:EY. Then, f is continuous if and only if limxpf(x)=f(p).

Proof \@{proof-of-function-is-continuous-at-point-iff-limit-at-point-equals-function-at-point}

Note that the definition of a function having a limit at point in a metric space is different from the definition of a function being continuous at a point in a metric space only in that the continuous definition requires the function to be defined at the point (and equal to the limit at the point.)

Theorem \@{fundamental-theorem-of-line-integrals}

When a line integral is known to be independent of path, and its value is required along some curve with initial point A and final point B, we can either replace the given curve with a simpler curve, or, take the difference in the values of the function ϕ(x,y) at B and A.

CP(x,y)dx+Q(x,y)dy={ϕ(x,y)}AB=ϕ(xB,yB)ϕ(xA,yA).

Theorem: Gibbs' Inequality \@{gibbs-inequality}

For discrete probability distributions p and q that share a support X,

D(p||q)0,

with equality iff p=q. Equivalently, cross-entropy is never less than entropy: H(p,q)H(p), with equality iff q=p.

Proof \@{proof-of-gibbs-inequality}

Because Log is Concave, by Jensen's Inequality we have that

Ep[logqipi]log(Ep[qipi])=log(i[piqipi])=log(iqi)=log1=0.

Then, since Ep[logqipi]=D(p||q), we have that D(p||q)0.

Now, note that if p=q, qipi=1 and D(p||q)=0. Conversely, suppose D(p||q)=0. Since log is strictly @concave, it is not @affine, and Jensen's Inequality condition for equality gives us that qipi=c. Now, iqi=icpi=1, and since p is a probability distribution, c=1, and therefore pi=qi and p=q.

Referenced by (1 direct)

Direct references:

Theorem \@{gradient-as-surface-normal-vector}

Let f be a differentiable scalar function in space. Let f(x,y,z)=c (with c constant) represent a surface S. Then, if the gradient of f at a point P of S is not the zero vector, it is a surface normal vector of S at P.

Proof \@{proof-of-gradient-as-surface-normal-vector}

Any curve C lying in S can be parameterized as r=[x(t),y(t),z(t)] such that

f(x(t),y(t),z(t))=c.(a)

Now, if we differentiate (a) with respect to t, we get

dfdt=fxx+fyy+fzz=(gradf)r=0.

Therefore, gradf is orthogonal to all the vectors r in the tangent plane of S at P, and is therefore a surface normal vector of S at P.

Theorem \@{gravitational-potential-is-a-solution-to-laplaces-equation}

The force of attraction

p=cr3r=c[xx0r3,yy0r3,zz0r3]

between two particles at points P0=(x0,y0,z0) and P=(x,y,z) (as given by Newton's law of gravitation) has the potential function f(x,y,z)=c/r, where r>0 is the distance between P0 and P.

Thus, p=gradf=grad(c/r). This potential function f is a solution of Laplace's Equation

2f=2fx2+2fy2+2fz2=0,

that is, f has a Laplacian of 0.

Theorem: Green's Theorem \@{greens-theorem}

Suppose P(x,y) and Q(x,y) have continuous first partial derivatives in a domain containing a simple, closed, piecewise smooth curve C and its interior R. Then

CPdx+Qdy=R(QxPy)dxdy.

Referenced by (2 direct)
Theorem: Heine-Borel \@{heine-borel}

If a set E in Rk has one of the following three properties, then it has the other two:

(a) E is closed and bounded.

(b) E is compact.

(c) Every infinite subset of E has a limit point in E.

Proof \@{proof-of-heine-borel}

If (a) holds, then EI for some k-cell I, and (b) follows from the facts that every k-cell is compact and closed subsets of compact sets are compact. Then, (c) follows from the fact that any infinite subset K of a compact set E has a limit point in E. To complete the cycle of implication, we must now show that (c) implies (a).

Assume, for the sake of contradiction, that E is not bounded. Then, E must contain an indexed set of points {xn},n=1,2,3, where each xn must satisfy |xn|>n. {xn} is obviously infinite, but we will show it has no limit points in E. Let pE. Then for some positive integer N, |p|<N. Since N is finite, there can only be finitely many points {qα} in {xn} with |xn|<N. If {qα} is empty, then p is obviously not a limit point of {xn}. Otherwise, let r=min{d(p,qα)}. Then r>0 and let Nr{p} be a neighborhood of p. Since no point in {xn}, other than perhaps p, lies in {xn}, p is clearly not a limit point of {xn}. Thus, our provisional assumption is invalid and (c) implies E is bounded.

To show that (c) implies E is closed, assume for the sake of contradiction that E is not closed. Then, there is a point x0Rk which is a limit point of E but is not in E. We will construct an infinite subset of E and show that it has no limit point in E. For n=1,2,3,, let the point xn be some point in E such that |xnx0|<1/n; let {xn} be the set of such points. {xn} is certainly infinite, because |xnx0| will eventually be bigger than 1/n for some n if we keep reusing the same xn infinitely many times. Now, {xn} has x0 as a limit point, and we will show it is its only limit point in Rk. Assume yRk,yx0. Then, via the triangle inequality, |xny||x0y||xnx0||x0y|1n12|x0y| for all but finitely many n, and thus y is not a limit point of {xn} because its its neighborhoods do not contain infinitely many points of {xn}. Thus, {xn} has no limit point in E, which contradicts (c), and therefore our provisional assumption that E is not closed is incorrect, and (c) implies that E is closed.

Note \@{heine-borel-note}

Without proof here, (b) and (c) are equivalent in any metric space, but (a) does not imply (b) and (c) in every metric space (we assumed Rk above.)

Theorem \@{homomorphism-injective-iff-trivial-kernel}

One useful fact you may recall from linear algebra that also applies with group homomorphisms is that ϕ is injective iff the kernel of ϕ is {e}.

from Groups
Theorem \@{infinite-subset-has-limit-point-implies-compact}

Let X be a metric space in which every infinite subset has a limit point. Then X is compact.

Proof \@{proof-of-infinite-subset-has-limit-point-implies-compact}

By Let X be a metric space in..., we know that X is separable and by Every separable metric space has a countable..., we know that X has a countable base. By the definition of base, we have that every open cover of X has a countable subcover {Gn},n=1,2,3,.

Suppose, for the sake of contradiction, that {Gn} has no finite subcollection that covers X. Let

Fn=(G1Gn)c.

Then, each Fn must be nonempty (otherwise a finite subcollection of {Gn} would cover X. However, since every point in X is in some Gn,

n=1Fn=.

Now, let E be a set with a point from each Fn. Since there are infinitely many Fn, E is an infinite subset of X, and therefore E has a limit point, p. Now, p must be in some open Gm, and so for some ϵ>0, Nϵ(p)Gm.

Now, note that each Fn+1Fn, because Fn+1 is formed by excluding all the points in Fn that are also in Gn+1. Because p is a limit point of E, Nϵp must contain infinitely many points of E. However, only finitely many points of E can be in Nϵ(p)Gm, because for n>=m, Fn contains no points in Gm. Therefore, p is not a limit point of E, and no such limit point can exist, contradicting our hypothesis that every infinite subset of X has a limit point. Therefore, E must be finite, and {Gn} must have a finite subcollection that covers X, meaning X is compact.

Theorem \@{infinite-subset-of-compact-set-has-limit-point}

If E is an infinite subset of a compact set K, then E has a limit point in K.

Proof \@{proof-of-infinite-subset-of-compact-set-has-limit-point}

Assume, for the sake of contradiction, that no point in K is a limit point of E. Then any point q in K has a neighborhood with at most one point in E; q, if qE. Since E is infinite, an infinite number of these singleton neighborhoods would be required to cover it, and therefore to cover K, since EK. But, this contradicts our hypothesis that K is compact. Therefore, our provisional assumption must be false, and K must contain a limit point of E.

Theorem \@{infinite-subset-of-countable-is-countable}

Every infinite subset E of a countably infinite set A is countable.

Proof \@{proof-of-infinite-subset-of-countable-is-countable}

Assume A is countably infinite, EA, and |E|=. Arrange a sequence {xn} from the distinct elements of A. Let n1 be the smallest positive integer such that xn1E, and then pick n2,,nk1 by assigning the next n in the sequence the index of the left-most entry in {xn} that has not yet been picked. Then, let nk be the smallest integer greater than nk1 such that xnkE. Now, {nk} is a sequence of strictly increasing positive integers giving us the indices of the first k elements of E in {xn}.

Now, define f:Z>0E as f(k)=xkn, which is a bijection between the positive integers and E, showing that E is countable.

Intuition \@{infinite-subset-of-countable-is-countable-intuition}

We can show this by putting A into a sequence {xn} of distinct values, so it can be indexed with the positive integers, and then constructing a subsequence of {xn} that are only the indices of elements of E.

As an example, consider the even numbers {0,2,4,} as a subset of the non-negative integers {0,1,2,}. Then, the indices of the even numbers are {1,3,5,}, and f(1)=0,f(1)=2,.

Note \@{infinite-subset-of-countable-is-countable-note}

This means that countably infinite sets are the smallest infinite sets. Any infinite subset of one has the same cardinal number as the parent set, and the same cardinal number as the set of natural numbers - 0 - "aleph null."

Theorem \@{integral-of-one-over-z-around-unit-circle}

If C is the unit circle, then C1zdz=2πi.

Proof \@{proof-of-integral-of-one-over-z-around-unit-circle}

First, note that we can parameterize C, the unit circle, as

z(θ)=eiθ,0θ<2π.

Now, making the substitution,

f(z(θ))=1e(iθ)=eiθ.

Now, using the definition of the contour integral,

Cf(z)dz=αβf[z(t)]z(t)dt

we have, noting that z(θ)=ieiθ,

C1zdz=02πeiθieiθdθ=i02πdθ=2πi.

Referenced by (1 direct)

Direct references:

Theorem \@{intersection-of-sequence-of-nested-intervals-is-nonempty}

If In is a sequence of intervals in R1, such that In+1In,n=1,2,3,..., then i=1nIn is not empty.

Proof \@{proof-of-intersection-of-sequence-of-nested-intervals-is-nonempty}

Let In=[an,bn], and let E be the set of all an. Then, E is nonempty, because even if an=bn for all n, it at least contains a single point. It is also bounded above by b1, since any bn is in [a1,b1]. Let x=supE. Let m and n be positive integers and we have that anam+nbm+nbm, so that xbm for each m. Since amx, we have that amxbm, that is, xIm for all m=1,2,3,, so xi=1nIn and thus i=1nIn is not empty.

Referenced by (1 direct)
Theorem \@{invariance-of-curl}

curlv is a vector. It has a length and a direction that are independent of the particular choice of a Cartesian coordinate system in space.

Theorem: Jensen's Inequality \@{jensens-inequality}

Let X be a random variable with finite expectation E[X], and let φ:RR be a convex function. Then

φ(E[X])E[φ(X)].(a)

If φ is concave, the inequality reverses:

φ(E[X])E[φ(X)].

In either case, equality holds if and only if φ is @affine on the support of X, or X is almost surely constant.

Proof \@{proof-of-jensens-inequality}

We will proceed by induction. Assume X is discrete and takes on n values x1,,xn with probabilities p1,,pn.

Base step, for n=2: Assume X takes on two distinct values, x1 and x2, with probabilities p1=λ,p2=1λ. Then we want to show that

φ(E[X])E[φ(X)]φ(λx1+(1λ)x2)λφ(x1)+(1λ)φ(x2).

The last line is just the definition of a convex function, so it's obviously true.

Inductive step: Assume that (a) holds for any n1 points. Take n points with weights pi summing to 1. Let

w=i=1n1pi=1pn.

Assuming w>0, let qi=pi/w. Then, qi>0,qi=1, so the qi form an (n1)-point distribution. Now, we'll rewrite E[X] to bundle the first n1 terms separately from the last term:

E[X]=i=1npixi=wi=1n1qixi+pnxn=wx+pnxn,(b)

where x=i=1n1qixi. Now, because w+pn=1, (b) is a two-point convex combination of x and xn. From our base case,

φ(i=1npixi)=φ(wx+pnxn)wφ(x)+pnφ(xn).

Now, from our inductive hypothesis we have that

φ(x)=φ(i=1n1qixi)i=1n1qiφ(xi).

Tying it all together we have

φ(E[X])=φ(i=1npixi)wi=1n1qiφ(xi)+pnφ(xn)=i=1n1piφ(xi)+pnφ(xn)=i=1npiφ(xi)=E[φ(X)].

TODO: show the equality case only holds when the support is affine.

Theorem \@{joint-entropy-is-less-than-or-equal-to-entropy-of-parts}

Let X and Y be discrete random variables with alphabets X and Y, and let p(x,y) be the probability of the joint occurrence of X=x and Y=y.

The entropy of the joint event (the joint entropy), H(X,Y), is less than or equal to the sum of the individual entropies, i.e.

H(X,Y)H(X)+H(Y),

with equality iff X and Y are independent, that is, iff p(x,y)=p(x)p(y).

Proof \@{proof-of-joint-entropy-is-less-than-or-equal-to-entropy-of-parts}

The entropy of the joint event is

H(X,Y)=x,yp(x,y)logp(x,y).

This is just treating each possible pair of individual outcomes as its own outcome, i.e. we have |X|×|Y| possible outcomes. Writing p(x)=yp(x,y) and p(y)=xp(x,y) for the marginals (the argument names the distribution), we have

H(X)=x,yp(x,y)logyp(x,y),H(Y)=x,yp(x,y)logxp(x,y).

So,

H(X)+H(Y)H(X,Y)=x,yp(x,y)logyp(x,y)x,yp(x,y)logxp(x,y)+x,yp(x,y)logp(x,y)=x,yp(x,y)(logp(x,y)logyp(x,y)logxp(x,y))=x,yp(x,y)(logp(x,y)logp(x)logp(y))=x,yp(x,y)(logp(x,y)p(x)p(y))=x,yp(x,y)(logp(x)p(y)p(x,y))

Now, let's define a new random variable for convenience, Z=p(X)p(Y)p(X,Y). We now have that

H(X)+H(Y)H(X,Y)=x,yp(x,y)(logp(x)p(y)p(x,y))=x,yp(x,y)(logZ)=E[logZ].

Now, by Jensen's Inequality, because Log is Concave, we have that E[logZ]logE[Z]. Now,

E[Z]=x,yp(x,y)p(x)p(y)p(x,y)=x,yp(x)p(y)=(xp(x))(yp(y))=11=1. Therefore logE[Z]=log1=0, and we have that E[logZ]0E[logZ]0, and

H(X)+H(Y)H(X,Y)0H(X,Y)H(X)H(Y)H(X,Y)H(X)+H(Y).

Now, for the equality part. Suppose H(X)+H(Y)=H(X,Y). Then, E[logZ]=0, and recalling that E[Z]=1, E[logZ]=logE[Z]=0. In Jensen's Inequality, equality holds only if our function is @affine or if Z is constant; since log is @affine on no interval, Z must be constant, and since E[Z]=1, Z=p(x)p(y)p(x,y)=1p(x)p(y)=p(x,y).

Now, suppose p(x)p(y)=p(x,y). Then Z=1 and

H(X)+H(Y)H(X,Y)=x,yp(x,y)(log1)=0,

so H(X)+H(Y)=H(X,Y).

Referenced by (1 direct)

Direct references:

Theorem \@{lagrange-theorem-for-indices}

Suppose H and K are subgroups of a group G such that KHG, and suppose that (H:K) and (G:H) are both finite. Then (G:K)=(G:H)(H:K) is finite.

Intuition \@{lagrange-theorem-for-indices-intuition}

The intuition here is that each coset of H can be partitioned into (H:K) cosets of K, and that G can be partitioned into (G:H) cosets of H, so G can be partitioned into (G:K)=(G:H)(H:K) cosets of K. Since both (G:H) and (H:K) are given as finite, their product is also finite.

Proof \@{proof-of-lagrange-theorem-for-indices}

In more detail, say G can be partitioned into n=(G:H) cosets of H, where n is a natural number. Let A be a set of coset representatives AG such that P=aiAaiH is a partition of G formed by the left cosets of H.

Then, P is

P={a1H,a2H,a3H,,anH},(a)

and each aiH is a disjoint coset of H in G.

Now, H can be partitioned into m=(H:K) cosets of K, where m is a natural number. Let B be a set of coset representatives BH such that Q=biBbiK is a partition of H formed by the left cosets of K.

Then, Q={b1K,b2K,b3K,,bmK}. Now, we can recover H by taking the union of the elements of Q, that is, H=qQq, so the elements of Q, after a round of flattening, are the same as those of H. If we replace H in (a) with Q, distribute each an group action over the elements of Q, and then take the union across each resulting set we end up with

R={a1b1K,a1b2K,a1b3K,,a1bmK,a2b1K,a2b2K,a2b3K,,a2bmK,a3b1K,a3b2K,a3b3K,,a3bmK,,anb1K,anb2K,anb3K,,anbmK},

which is G partitioned into the left cosets of K. Because we constructed each element in R by partitioning the elements of a partition of G into cosets of K (then taking their union), we know they are disjoint and cover all of G. There are nm=(G:H)(H:K)=(G:K) elements in R, and since n and m are finite, so is (G:K).

from Groups

If p^ is the proportion of successes in a random sample of size n and q^=1p^, an approximate 100(1α)% confidence interval for the binomial parameter p is given by

p^zα/2p^q^n<p<p^+zα/2p^q^n.

Note \@{ci-proportion-note}

When n is small and the unknown proportion p is believed to be close to 0 or 1, this approach doesn't work well and shouldn't be used. This approach should only be used when both np^ and nq^ are greater than or equal to 5.

Theorem: Law of Large Numbers \@{law-of-large-numbers}

Suppose X1,X2, are independent and identically distributed random variables with finite mean μ. Then for any ϵ>0,

limnP(|X1++Xnnμ|ϵ)=0.

Note \@{law-of-large-numbers-note}

Intuitively, this means that the more identically distributed random variables we have, the closer their average value will get to the mean for the random variables. If we think of each random variable as an identical sample from the same population, another way to think of this is the more samples we get, the closer the average value across all samples will be to the true average value for the population, and we can get as close as we like to the true average value for the population by taking more samples.

Proof \@{proof-of-law-of-large-numbers}

We assume that the variance of the random variables is also finite. Then, note that

E[X1+Xnn]=μ,

and

V[X1+Xnn]=σ2n.

Then, by Chebyshev's Inequality, we have

P(|X1++Xnnμ|ϵ)σ2nϵ2.

Now, as n increases, the term on the right approaches 0, which by the Squeeze Theorem implies the term on the left also approaches 0, and is 0 at the limit.

Theorem \@{limit-of-a-function-characterized-by-limits-of-sequences}

Let X and Y be metric spaces; suppose EX, f:EY, and p is a limit point of E. Then

limxpf(x)=q(a)

if and only if

limnf(pn)=q(b)

for every sequence {pn} in E such that

pnp,limnpn=p.(c)

Proof contrapositive \@{proof-of-limit-of-a-function-characterized-by-limits-of-sequences}

Suppose that (a) is true and let {pn}E be a sequence such that (c) holds. Let ϵ>0. Then, for some δ>0, if dX(x,p)<δ, then dY(f(x),q)<ϵ. Now, there is also some N such that dX(pn,p)<δ whenever nN, and thus, dY(f(pn),q)<ϵ whenever nN, so (b) holds.

Conversely, suppose that (a) is false. Then, for some ϵ>0, that for every δ>0 there exists a point xE such that dY(f(x),q)ϵ but 0<dX(x,p)<δ. If we let δn=1/n,n=1,2,3,, and each xn a point such that 0<dX(xn,p)<δn, then {xn} is a sequence satisfying (c). However, since dY(f(xn),q)ϵ for all n, (b) does not hold.

Corollary \@{limit-of-a-function-at-a-point-is-unique-if-it-exists}

If f has a limit at p, this limit is unique.

Proof \@{proof-of-limit-of-a-function-at-a-point-is-unique-if-it-exists}
Theorem \@{limit-of-diameter-of-remaining-points-in-cauchy-sequence-is-zero}

if {pn} is a sequence in X and if EN consists of the points pN,pN+1,pN+2,, then {pn} is a Cauchy sequence if and only if

limNdiamEN=0.

Proof \@{proof-of-limit-of-diameter-of-remaining-points-in-cauchy-sequence-is-zero}

Suppose {pn} is a Cauchy sequence. Then, let ϵ>0. For some integer N, d(pn,pm)<ϵ when n,mN. Therefore, diamEN<ϵ. Since ϵ was arbitrary, we can see that the sequence {diamEN} converges to 0.

Conversely, suppose limNdiamEN=0. Then, every neighborhood of 0 contains {diamEN} for all but finitely many N. Let ϵ>0. Then, pick N such that diamEn<ϵ. Letting m,nN, we have that d(pm,pn)diamEn<ϵ.

Referenced by (1 direct, 1 transitive)
Theorem \@{limit-point-implies-convergent-sequence}

If EX and if p is a limit point of E, then there is a sequence {pn} in E such that p=limnpn.

Proof \@{proof-of-limit-point-implies-convergent-sequence}

For each n=1,2,3, there is a point pnE such that d(p,pn)<1/n. Let ϵ>0, and pick N so that Nϵ>1. Then, if n>N, d(p,pn)<ϵ, so limnpn=p.

Theorem \@{limit-points-form-closed-set}

Let E be the set of all limit points of a set E in space X. Then E is closed.

Proof \@{proof-of-limit-points-form-closed-set}

Let pX,pE. Then some neighborhood N of p contains no points in E, other than possibly p itself. If N contains only p, then p is not a limit point of E. Suppose, for the sake of contradiction, some point qN,qp is a limit point of E. Then, every neighborhood of q contains some point m in E. Let MN,pM, be such a neighborhood, and let mM,mq be a limit point of E. Now, m has neighborhoods wholly in N, and such neighborhoods can have no point in E, so we have a contradiction, and therefore q is not a limit point of E. Hence, p is an interior point of Ec, and Ec is open, and therefore E is closed.

Theorem \@{limits-of-sequences-are-unique}

If pX,pX and {pn} converges to p and p, then p=p.

Proof \@{proof-of-limits-of-sequences-are-unique}

Suppose, for contradiction, that pp. Then, ϵ=d(p,p)>0. Let δ=ϵ/2 and Bδ(p),Bδ(p) be balls around p and p, respectively. This means that only finitely many points from {pn} are not in Bδ(p). However, since Bδ(p) and Bδ(p) are disjoint by construction, this means only finitely many points of {pn} are in Bδ(p), a contradiction. Therefore, our assumption that pp is incorrect, so p=p.

Theorem \@{line-integral-over-a-plane-curve}

If f is a continuous function of two variables x=x(t),y=y(t) whose domain includes the smooth curve C, then the line integral can be evaluated as:

Cf(x,y)ds=abf(x(t),y(t))(dxdt)2+(dydt)2dt

Theorem \@{linear-stability-analysis}

Let x be a fixed point of x˙=f(x). Then, if f(x)0, if f(x) is negative, then x is a stable fixed point . If f(x) is positive, then x is an unstable fixed point.

Remark \@{linear-stability-analysis-remark}

This comes from letting u(t)=x(t)x be a small perturbation away from x, differentiating it, writing its taylor series, then noticing that f(x)=0 and terms greater than the linear term matter less than the linear term and writing

u˙=f(x)u.

This is called the linearization about x.

It also only works if f(x)0. If that's not the case, the best bet is to fall back to graphical analysis or to solve explicitly if possible.

Theorem: Log is Concave \@{log-is-concave}

The log function is @concave, i.e. log is @convex.

Proof \@{proof-of-log-is-concave}

The second derivative of natural log is 1x2, which is always non-positive. By @second-derivative-test-for-convexity, log is therefore @concave.

Theorem \@{mapping-continuous-iff-inverse-images-of-open-sets-are-open}

A mapping f of a metric space X into a metric space Y is continuous on X if and only if f1(V) is open in X for every open set V in Y (see inverse image.)

Proof \@{proof-of-mapping-continuous-iff-inverse-images-of-open-sets-are-open}

Assume f is continuous on X and V is an open set in Y. Suppose, for the sake of contradiction, that f1(V) is not open. Then, some point pf1(V) is not an interior point of f1(V), which means there is no neighborhood of p that contains only points in f1(V), that is, every neighborhood of p contains some point q that is not in f1(V), i.e., f(q)V. Now, since V is open and f(p)V, there is some ϵ>0 for which Bϵ(f(p))V, but, since all neighborhoods of p contain some qf1(V), there is no δ>0 for which all points within δ of p are mapped to Bϵ(f(p)) by f, a contradiction, since f is continuous on X by hypothesis. Therefore, our assumption is incorrect and f1(V) is open.

Conversely, suppose f1(V) is open in X for every open set V in Y. Let V be an open set in Y. Assume, for the sake of contradiction that there is some pf1(V) at which f is not continuous. Let ϵ>0. Then, there is no δ>0 for which Bδ(p) contains only points that are mapped by f to V, i.e. every neighborhood of p contains some point that is not in f1(V), and therefore p is not an interior point of f1(V), and f1(V) is not open, a contradiction. Thus, our assumption must be incorrect, and there is no such p, and since entire space Y is an open subset of itself, f must be continuous on all of X.

Theorem: Markov's Inequality \@{markovs-inequality}

Let X be a non-negative random variable (X0,) and t>0. Then,

P(Xt)E[X]t.

Note \@{markovs-inequality-note}

That is, the probability that X is at least t has an upper-bound of the expectation (mean) of X divided by t. In simpler terms, the probability that X>t can't be too big.

Note that as t grows larger, the probability of X being greater than t grows smaller.

Proof \@{proof-of-markovs-inequality}

This follows from the Law of Total Expectation, which is

E[X]=E[XXt]P(Xt)+E[XX<t]P(X<t).(a)

Here, we're partitioning X's sample space into a portion above t and a portion below t.

Now, E[XXt] is the expected value when Xt, which is necessarily at least t, since X is restricted to taking on values of at least t. So, we have that E[XXt]t.

We can substitute this into (a) to get

E[X]tP(Xt)+E[XX<t]P(X<t).(b)

Note that our equation has turned into an inequality; we must do this because the original RHS was equal to the original LHS, and the original RHS is greater than or equal to our new RHS, which means the original LHS is greater than or equal to our new RHS.

Now, since we said that X is non-negative, its expected value is also non-negative, including when X<t. We have by definition that P(X<t) is non-negative (because all probabilities are non-negative). Therefore, E[XX<t]P(X<t) must also be non-negative as it's the product of two non-negative numbers. Since the LHS of (b) is greater than the RHS with this non-negative value added to the RHS, the LHS is also bigger without it added, and therefore we can drop it to get

E[X]tP(Xt)(c)

Dividing both sides by t and swapping sides gives us

P(Xt)E[X]t.

Theorem: Mean Value Theorem \@{theorem-1}

Suppose f is continuous on [a,b] and differentiable on (a,b). Then there exists a point c in (a,b) such that:

f(c)=f(b)f(a)ba.

This means that for a function continuous on an interval, and differentiable on that interval except maybe at its endpoints, there is a point on the interval where the derivative of the function at that point equals the slope of the function between its endpoints.

Theorem: Moment Generating Function of a Sum \@{mgf-of-sum}

The moment generating function for the sum of two random variables is the product of the moment generating functions of the two random variables. More generally, if X1,X2,Xn are independent random variables, then

MX1+X2++Xn(t)=MX1(t)MX2(t)MXn(t).

Proof \@{proof-of-mgf-of-sum}

We'll prove the case where n=2; the general proof is exactly the same but more tedious.

MX+Y(t)=E[et(X+Y)]=E[etXetY]=E[etX]E[etY]=MX(t)MY(t).

Theorem \@{n-tuples-of-countable-elements-are-countable}

Let A be a countable set, and let Bn be the set of all n-tuples (a1,,an) where akA(k=1,,n), and the elements a1,,an need not be distinct. Then Bn is countable.

Proof \@{proof-of-n-tuples-of-countable-elements-are-countable}

We will proceed using proof by induction. First, for the base case, note that B1 is the set of 1-tuples formed by elements of A, so B1=A and is thus countable. Now, for the inductive step, assume Bn1 is countable (n=2,3,4,). Then we have that

Bn={(b,a)|bBn1,aA}=bBn1(b×A).

So, for any given n1-tuple b, we form n-tuples by appending each element of a to it, and so the set of pairs (b,a) has the same cardinality as A, and is thus countable. Bn is thus the union of the countable set of countable sets (the set of sets formed by appending each element of A to each element of Bn1) and is therefore countable itself, by a theorem proved above. Therefore, by induction, every Bn is countable.

Corollary \@{rationals-are-countable}

The set of rational numbers is countable.

Proof \@{proof-of-rationals-are-countable}

Rational numbers just formed from pairs of integers: (a,b)a/b,b0, so we use the above theorem with n=2.

Referenced by (1 direct)
Theorem \@{negation-of-conjunction-is-disjunction-of-negations}

The negation of a conjunction is the disjunction of negations.

Proof \@{proof-of-negation-of-conjunction-is-disjunction-of-negations}

Let A and B be boolean variables. We want to show that

¬(AB)¬A¬B.

First, assume ¬(AB) is true. Then, both AB must be false, so either A must be false or B must be false, or both must be false. If A is false, then ¬A is true, and so is ¬A¬B. The same is true if B is false, so ¬(AB)¬A¬B.

Now, assume ¬A¬B is true. Then, either ¬A or ¬B must be true, so either A or B or both must be false. Now, if A is false, then AB is false. The same holds if B is false, and thus ¬(AB) is true. Therefore ¬A¬B¬(AB) and we have shown ¬(AB)¬A¬B.

Theorem \@{negation-of-disjunction-is-conjunction-of-negations}

The negation of a disjunction is the conjunction of negations.

Proof \@{proof-of-negation-of-disjunction-is-conjunction-of-negations}

Let A and B be boolean variables. We want to show that

¬(AB)¬A¬B.

First, assume ¬(AB) is true. Then, AB is false. If A were true, then we'd have a contradiction, and similarly with B, so both A and B must be false, that is, ¬A and ¬B most both be true, and ¬(AB)¬A¬B.

Now, assume ¬A¬B. Here, both ¬A and ¬B must be true, so both A and B must be false. Therefore, AB is false, so ¬A¬B¬(AB) and we have shown ¬(AB)¬A¬B.

Theorem \@{neighborhood-of-limit-point-contains-infinitely-many-points}

If p is a limit point of a set E, then every neighborhood of p contains infinitely many points of E.

Proof \@{proof-of-neighborhood-of-limit-point-contains-infinitely-many-points}

Let p be a limit point of E and let Nr(p) be a neighborhood of p. Suppose that Nr(p) contains only finitely many points of E. Since we have finitely many points, we can inspect each and find the minimum distance from p to any point in Nr(p)E{p} and call it s. Now, we can make a new neighborhood Ns(p), which contains none of the points in Nr(p)E{p} since they're all at least s away from p, by construction. But then, p is not a limit point of E, since it has a neighborhood that contains no points of E{p} Therefore, we have a contradiction, and Nr(p) must therefore contain infinitely many points.

Corollary \@{only-infinite-sets-have-limit-points}

From this, it's evident that a finite set of points has no limit points. That is, if a set has a limit point, then the set if infinite.

Theorem \@{nested-sequence-of-compact-sets-with-lim-diam-zero-has-singleton-intersection}

If Kn is a sequence of nonempty compact sets in X such that Kn+1Kn,(n=1,2,3,) and if

limndiamKn=0,

then 1Kn consists of exactly one point.

Proof \@{proof-of-nested-sequence-of-compact-sets-with-lim-diam-zero-has-singleton-intersection}

Let K=1Kn. Then K is not empty. Assume for the sake of contradiction that K contains more than one point. Then, diamK>0. But, for each n,KKn, so that diamKndiamK. But, this contradicts our given that lim+ndiamKn=0, so our assumption that K contains more than one point must be invalid, and thus K contains exactly one point.

Referenced by (1 direct, 1 transitive)

Given a communication channel which has a capacity of C bits per second, accepting signals from a source of entropy (or information) of H bits per symbols, it is possible, given a properly devised coding procedure, for the transmitter to transmit symbols over the channel at an average rate which is nearly C/H but which, no matter how clever the coding, can never exceed C/H.

Theorem \@{non-empty-perfect-sets-in-rk-are-uncountable}

Let P be a nonempty perfect set in Rk. Then P is uncountable.

Proof \@{proof-of-non-empty-perfect-sets-in-rk-are-uncountable}

We know that P is infinite, because by definition, all points in perfect sets are limit points, and only infinite sets have limit points.

Suppose, for the sake of contradiction, that P is countable. Label the points of P as x1,x2,. We will construct a sequence of Vn of neighborhoods.

As a base step, let V1 be any neighborhood of x1; let V1={yRk| |yx1|<r} (note: subsequent Vn+1 aren't required to be neighborhoods of xn+1.) Then the closure V1 of V1 is V1={yRk| |yx1|r}.

For the inductive step, suppose as an induction hypothesis that we have some Vn that's been constructed such that VnP is not empty. Since every point of P is a limit point of P, we can make a neighborhood Vn+1 such that (i) Vn+1Vn, (ii) xnVn+1, (iii) Vn+1P is not empty. Now, Vn+1 satisfies our induction hypothesis, and since V1 does too, we have Vn defined for all n=1,2,3,.

For each n, let Kn=VnP. Since Vn is closed and bounded, Vn is compact. Since xnVn+1, no point of P lies in n=1Kn. Since KnP, this implies that n=1Kn is empty. But, each Kn is nonempty, by (iii), and Kn+1K, by (i). But the intersection of nonempty compact nested sets is nonempty, so we have a contradiction, so our provisional assumption that P is countable must be incorrect. Therefore, P is uncountable.

Corollary \@{every-interval-is-uncountable}

Every interval [a,b](a<b) is uncountable, and thus the set of all real numbers is uncountable as it contains uncountable subsets.

Referenced by (1 direct)

Direct references:

Theorem \@{nonempty-intersection-of-finitely-many-compact-sets}

If {Kα} is a collection of compact subsets of a metric space X such that the intersection of every finite subcollection of {Kα} is nonempty, then Kα is nonempty.

Proof \@{proof-of-nonempty-intersection-of-finitely-many-compact-sets}

Let Gα=Kαc for each α, and note that since Kα is compact and therefore closed, Gα is open. Then, fix a member K1 of {Kα}. Assume, for contradiction's sake, that no point of K1 is in all Kα, that is, that Kα=. Then, any point xK1 is in some Kαc=Gα, so {Gα} forms an open cover of K1. Since K1 is compact, some finite subset Gα1,,Gαn of {Gα} forms a finite subcover of K1 such that K1Gα1Gαn=(Kα1Kαn)c (by De Morgan's.) Therefore K1Kα1Kαn=. This is an empty intersection of a finite subcollection of {Kα}, which contradicts our hypothesis that all finite intersections are nonempty. Therefore, our assumption that no point in K1 is in all Kα is incorrect, and some point in K1 is in all Kα, and therefore Kα is not empty.

Corollary \@{intersection-of-nonempty-nested-compact-sets-is-nonempty}

If {Kα} is a sequence of nonempty compact sets such that Kn+1Kn,n=1,2,3,, then i=1Kn is not empty.

Proof \@{proof-of-intersection-of-nonempty-nested-compact-sets-is-nonempty}

Suppose xKn,n2. Then, by definition, xKn1, and by induction, xK1. Then, every Kα is a nonempty subset of K1, and so the intersection of any finite number of these Kα will be nonempty, and by If {Kα} is a collection of compact..., i=1Kn is not empty.

Theorem: Normal Approximation to the Binomial \@{normal-approximation-binomial}

If X is a binomial random variable with mean μ=np and variance σ2=npq, then

Z=limnXnpnpq=n(z;0,1).

This works well when n is large and p is not extremely close to 0 or 1, and also works well when n is small and p is near 0.5.

Let X be a binomial random variable with parameters n and p. For large n, X has approximately a normal distribution with μ=np and σ2=npq=np(1p) and

P(Xx)=k=0xb(k;n,p)area under normal curve to the left of x+0.5=P(Zx+0.5npnpq)

and the approximation is good if np and n(1p) are greater than or equal to 5.

Note: Continuity Correction \@{continuity-correction}

The 0.5 is called a continuity correction and comes from the fact that

P(X=k)P(k0.5Xk+0.5).

Theorem: nth root of z \@{nth-root-of-z}

For complex z and integer n, with z=r(cosx+isinx), we have

r1n(cosx+2πkn+isinx+2πkn).

Theorem \@{open-iff-complement-closed}

A set E is open iff its complement is closed.

Proof \@{proof-of-open-iff-complement-closed}

First, consider the case that Ec is empty, and therefore open. If Ec has no limit points, it is vacuously closed. Suppose Ec has a limit point x. Since E is empty, x must be in Ec, therefore x is closed. Now, consider the case that Ec is empty, and therefore closed. If E is empty, it is open, and the theorem is satisfied. If E is not empty, a point x in E has only points in E in any neighborhood, since all points are in E, and therefore E is open.

Now we deal with the cases where neither E nor Ec are empty.

Now, let Ec be closed. Let xE. Since Ec is closed, x is not a limit point of Ec, that is x has some neighborhood that doesn't contain a point in Ec and must therefore be a subset of E. Therefore, x is an interior point of E, and E is open.

Conversely, assume E is open. Let x be a limit point of Ec. Suppose, for the sake of contradiction, that xE. Then, since E is open, x is an interior point of E and has some neighborhood that is a subset of E. This is a contradiction, since every neighborhood of x must contain at least one point of Ec to be a limit point of Ec. Therefore, x must be in Ec, and it follows that Ec is closed.

Theorem \@{open-relative-iff-intersection-with-open-subset}

Suppose YX. A subset E of Y is open relative to Y iff E=YG for some open subset G of X.

Proof \@{proof-of-open-relative-iff-intersection-with-open-subset}

Suppose E is open relative to Y. Then, for every pE, there is some rp>0 such that d(p,q)<rp, qX implies that qY. Let Vp be the set of all qX where d(p,q)<rp (and thus qY) and let

G=pEVp.

Then, since each Vp is an open subset of X, so is G. Now, since pVp for each pE, EGY. Also, since VpYE for every pE, GYE, and E=GY.

Conversely, suppose E=YG for some open subset G of X. Now, suppose pE. Then, pG, and there is some neighborhood VpG. Then, VpYE, so E is open relative to Y.

Referenced by (1 direct)
Theorem \@{open-set-in-r1-is-countable-union-of-disjoint-segments}

Every open set in R1 is the union of an at most countable union of disjoint segments.

Proof \@{proof-of-open-set-in-r1-is-countable-union-of-disjoint-segments}

Let E be an open set in R1. Let xE. Let a=inf{y:(y,x]E} and b=sup{y:[x,y)E}. Then, a<x<b, because E is open and thus x is an interior point of E and there is some neighborhood around x that is entirely within E. Let I(x)=(a,b). By construction, I(x) is connected. For all uI(x), I(u) must have the same end points as I(x), since if it extended beyond, our construction of I(x) would be contradicted. Also note that for all vEI(x), I(v)I(x)=, for if they intersected, they would form an open interval. Thus, each I(x),xE is either disjoint from all others or identical to some other I(y),yE, and E is the union of all such unique I.

Now, in each I, we can pick a rational number. Because the rationals are countable, we have at most countably many unique I, and E is their union.

Theorem \@{permutation-as-product-of-disjoint-cycles}

Every permutation σ of a finite set is a product of disjoint cycles.

from Groups
Theorem \@{permutation-parity-is-well-defined}

A permutation in Sn can be written as either a product of an odd number of transpositions or a product of an even number of transpositions, but not both.

from Groups
Theorem \@{permutations-form-group}

Let A be a nonempty set, and SA be the collection of all permutations of A. Then SA is a group under permutation multiplication.

from Groups
Theorem \@{poincare-bendixson}

Suppose that

(1) R is a closed, bounded subset of the plane;

(2) x˙=f(x) is a continuously differentiable vector field on an @open-set containing R;

(3) R does not contain any fixed points;

(4) There exists a trajectory C that is "confined" in R, in the sense that it starts in R and stays in R for all future time.

Then, either C is a closed orbit, or it spirals toward a closed orbit as t. In either case, R contains a closed orbit.

Referenced by (1 direct)

Direct references:

Theorem: Poisson Approximation to the Binomial \@{poisson-approximation-binomial}

Let X be a binomial random variable with probability distribution b(x,n,p). When n, p0, and limnnp=μ remains constant,

limnb(x,n,p)=p(x,μ).

Theorem \@{rationals-are-dense-in-reals}

The rationals are dense in the reals.

Proof \@{proof-of-rationals-are-dense-in-reals}

Let xR and ϵ>0. Pick nN such that 1/n<ϵ. Now, because the reals are Archimedean, for some mZ we have that m=nx. Then,

mnxm+1,mnxm+1n.

Now, if we let q=mn, we have

0xq<1n<ϵ,

so |xq|<ϵ. Therefore, every neighborhood of x contains some qQ, and so x is either a limit point of Q or else xQ, and so the rationals are therefore dense in the reals.

Corollary \@{rational-between-any-two-reals}

If x,yR, and x<y, then there exists a pQ such that x<p<y. That is, there is always a rational number between any two distinct real numbers.

Proof \@{proof-of-rational-between-any-two-reals}

Pick nN such that n(ya)>1. Because because the reals are Archimedean, there is some kZ such that nx<k<ny and x<n/k<y. Thus, q=n/kQ and r(x,y).

Theorem \@{reals-are-archimedean}
Theorem: Residue Theorem \@{residue-theorem}

Let f(z) be analytic inside a simple closed path C and on C, except for finitely many @singular-points z1,z2,,zk inside C. Then,

Cf(z)dz=2πij=1kResz=zjf(z).

Referenced by (1 direct)

Direct references:

Theorem \@{residue-at-mth-order-pole}

Resz=z0f(z)=1(m1)!limzz0{dm1dzm1[(zz0)mf(z)]}.

Theorem: Sample Size for Estimating a Mean \@{sample-size-mean}

If x¯ is used as an estimate of μ, we can be 100(1α)% confident that the error will not exceed a specified amount e when the sample size is

n=(zα/2σe)2.

Theorem: Sample Size for Estimating a Proportion \@{sample-size-proportion}

If p^ is used as an estimate of p, we can be 100(1α)% confident that the error will be less than a specified amount e when the sample size is approximately

n=zα/22p^q^e2.

Theorem: Second Derivative Test for Convexity \@{theorem-4}

Let φ be twice differentiable on an open interval (a,b). Then φ is @convex on (a,b) if and only if φ(x)0 for all x(a,b), and @concave if and only if φ(x)0 for all x(a,b).

Theorem: Second Simple Pole Residue Formula \@{second-simple-pole-residue-formula}

Resz=z0f(z)=Resz=z0p(z)q(z)=p(z0)q(z0).

Theorem \@{sequence-converges-iff-neighborhood-contains-all-but-finitely-many-points}

{pn} converges to pX if and only if every neighborhood of p contains pn for all but finitely many n.

Proof \@{proof-of-sequence-converges-iff-neighborhood-contains-all-but-finitely-many-points}

Suppose {pn} converges to pX. Let ϵ>0. For some integer N, d(p,pn)<ϵ when n>N. Therefore, pnBϵ(p) for all but the finitely many pn where nN.

Conversely, suppose every neighborhood of p contains all but finitely many pn, i.e., for all but N elements of {pn}. Let ϵ>0. Then, pnBϵ(p) whenever nN, therefore, d(p,pn)<ϵ.

Theorem \@{sequence-in-compact-metric-space-has-a-convergent-subsequence}

If {pn} is a sequence in a compact metric space X, then some subsequence of {pn} converges to a point in X.

Proof \@{proof-of-sequence-in-compact-metric-space-has-a-convergent-subsequence}

Let E be the range of {pn}. If E is finite, then at least one point p in E must be repeated infinitely many times in {pn}. If we let {ni} be the indices of the occurrences of p in {pn}:

pn1=pn2==p,

then the subsequence {pni} converges to p.

On the other hand, if E is infinite, then E has a limit point pX. Pick n1 so that d(p,pn1<1. Now, after picking n1,,ni1, we can pick ni>ni1 such that d(p,pni)<1/i, so {pni} converges to p.

Referenced by (1 direct)

Direct references:

Theorem \@{set-and-closure-have-same-limit-points}

If E is a set in a metric space, then E and E have the same limit points.

Proof \@{proof-of-set-and-closure-have-same-limit-points}

If E is closed, then we are done, because a set equals its closure if it is closed.

Suppose p is a limit point of E. Then every neighborhood of p contains some qE,qp. Since qE, qE, so p is a limit point of E.

Conversely, suppose p is a limit point of E. Then, every neighborhood N of p contains a point of qE,qp. If qE, then N clearly contains a point in E. Otherwise, qE,qnE. Now, since qN and every neighborhood is an open set, q has some neighborhood MN. Since qE, M contains some point sE. Since MN, sN, and therefore N contains a point in E. Thus, all neighborhoods of p contain some point in E, and p is a limit point of E.

Theorem \@{set-and-its-limit-points-may-have-different-limit-points}

A set E and its limit points E do not necessarily have the same limit points.

Proof \@{proof-of-set-and-its-limit-points-may-have-different-limit-points}

Consider P={1n|nN}. Then P has one limit point, 0, but P={0} has no limit points, since the only number that contains 0 in all its neighborhoods is 0 itself (see From this, it's evident that a finite....)

Theorem \@{set-equality-via-subset-inclusion}

Two sets X and Y are equal if and only if X is a subset of Y and Y is a subset of X.

Proof \@{proof-of-set-equality-via-subset-inclusion}

Suppose X and Y are sets with XY and YX. Now, suppose xX. Then, xY. Conversely, suppose yY. Then yX. Thus, (x)(xXxY), and X=Y.

Theorem \@{set-is-its-closure-iff-it-is-closed}

If X is a metric space and EX, then

(a) E is closed.

(b) E=E iff E is closed.

(c) EF for every closed set FX such that EF.

By (a) and (c), E is the smallest closed subset of X that contains E.

Proof \@{proof-of-set-is-its-closure-iff-it-is-closed}

(a) Suppose pX and pE. Then p is not in E and is not in E, and is in fact in Ec. Now, since p is not a limit point of E, it has some neighborhood Nr(p) that does not intersect E. Any point x in Nr(p) is an interior point of Nr(p), and therefore has its own neighborhood Nϵ(x) that does not intersect E, and therefore x is not a limit point of E. Thus, any point in Ec is an interior point of Ec, and Ec is therefore open and its complement, E, is closed (since a set is open iff its complement is closed.)

(b) Suppose E is closed. Then it contains its limit points, so E=EE=E. Conversely, suppose E=E. By (a), E is closed.

(c) Suppose that EFX, and that F is closed. Suppose pE. If pE, then pF because EF. If pE, then it must be in F also, since F contains all points of E, and is closed, and thus must contain the limit points of E.

Theorem: Simple Poles on the Real Axis \@{theorem-3}

If f(z) has a simple pole at z=a on the real axis, then

limr0C2f(z)dz=πiResz=af(z),

as illustrated below

Principal Value

Theorem: Special case of Blaire's theorem \@{baire-category-theorem-special-case}

If Rk=n=1Fn, where each Fn is a closed subset of Rk, then at least one Fn has a non-empty interior. Equivalently, If Gn is a dense open subset of Rk, for n=1,2,3,, then n=1Gn is not empty (in fact, it is dense in Rk.)

Proof \@{proof-of-baire-category-theorem-special-case}

First, note that since Rk=n=1Fn, every point in Rk is in some Fn, and so n=1Fnc must be empty.

Suppose, for the sake of contradiction, that no Fn has a non-empty interior, that is, every Fn is closed with an empty interior. Then, each Fnc is open. Moreover, since Fn has an empty interior, it has no points for which there exists a neighborhood that contains only points in Fn, that is, every neighborhood of each point of Fn contains a point in Fnc, so every point in Fn is a limit point of Fnc, and every point in Rk is either in Fnc or is a limit point of Fnc, so each Fnc is non-empty and dense in Rk.

As a base step, let x1 be some point in Rk, and let B1 be an open ball around x1. Since the interior of F1 is empty, B1F1c is not empty, and is open.

For the inductive step, suppose we have some Bn that's constructed such that BnFnc is non-empty and open. Then, we can pick a point xn+1BnFnc,xn+1xn, and make a ball Bn+1 around it such that Bn+1BnFnc, xnBn+1, and Bn+1Fn+1c is open and not empty. Now, since Bn+1 satisfies our induction hypothesis, and since B1 does as well, we have Bn defined for all n=1,2,3,.

Since the set of points {xn} is infinite (by induction) and bounded (all points are within B1,) it has a limit point p in Rk.. Now, suppose, for contradiction, that p is not in every Fnc. Then, for some N, p is not in FNc. Since pFNc and BN+1FNc, we have pBN+1. Since BN+1 is open and p is outside it, p is at some positive distance ε from any point qBN+1. But xnBnBN+1 for all n>N, so all these infinitely many points are at distance at least ε from p. Thus any neighborhood of p with radius less than ε contains at most finitely many points of {xn}, contradicting that p is a limit point.

Now, this means that pn=1Fnc, meaning n=1Fnc, a contradiction! Therefore, our supposition that every Fn has an empty interior must be incorrect, and some Fn must have a non-empty interior.

Theorem: Stationary Distribution of an Ergodic Chain \@{stationary-distribution}

When the markov chain is ergodic, the limit

πk=limnpjk(n)

exists and

k=1Nπk=1.

Note \@{stationary-distribution-note}

Here, π=π1,π2,,πN and the entries in it represent the long term probabilities of being in any given state, or equivalently, the portion of time spent in any given state.

Note that πP=π, which when combined with the fact that the entries of π sum to 1 lets us solve a linear system of equations to find the entries of π.

Theorem: Stoke's Theorem \@{theorem-16}

Let S be a @piecewise smooth oriented surface in space and let the boundary of S be a piecewise smooth simple closed curve C. Let F(x,y,z) be a continuous vector function that has continuous first partial derivatives in a domain in space containing S. Then

S(curlF)ndA=CFr(s)ds.(2)

Here n is a unit @normal-vector of S. r=dr/ds is the unit tangent vector and s the arc length of C.

In components, (2) becomes

R[(F3yF2z)N1+(F1zF3x)N2+(F2xF1y)N3]dudv=C(F1dx+F2dy+F3dz).

Here, F=[F1,F2,F3],N=[N1,N2,N3],ndA=Ndudv,rds=[dx,dy,dz], and R is the region with boundary curve C in the uv-plane corresponding to S represented by r(u,v).

Start with a source of n states a1,,an, emitted by a @stationary ergodic Markov chain with transition probabilities pik=Pr(Xt+1=akXt=ai),k=1npik=1, and Stationary Distribution of an Ergodic Chain P=(P1,,Pn). Its entropy rate is, by definition, H=i=1nk=1nPipiklogpik. A message is a sequence S=(x1,,xN) of length N, each xt{a1,,an}. Such a message is a path through the network with probability pS=Px1t=1N1pxtxt+1. Let ϵ,δ>0. Then there exists N0 such that for all NN0 the length-N sequences split into two classes:

  1. The typical set, Aδ(N)={S:|1Nlog1pSH|δ}.
  2. The atypical set, Aδ(N), with total probability Pr(Aδ(N))<ϵ.

Moreover the convergence holds @almost surely: Pr(limN1Nlog1pS=H)=1, so with probability 1 a drawn sequence is typical for all sufficiently large N

Proof \@{proof-of-theorem-5}

--- the left eigenvector solving P=PT for the transition matrix T=(pik) (eigenvalue 1, normalized so iPi=1).

Theorem \@{subsequential-limits-of-a-metric-space-form-a-closed-set}

The subsequential limits of a sequence {pn} in a metric space X form a closed set subset of X.

Proof \@{proof-of-subsequential-limits-of-a-metric-space-form-a-closed-set}

Let E be the set of all subsequential limits of {pn} and let q be a limit point of E. We want to show that qE.

First, note that if the range of {pn} is just {q}, then q is the only subsequential limit of {pn}. In this case, E={q} is a singleton and is closed set, as it vacuously contains all of its limit points. So, assume this is not the case.

Choose n1 so that pn1q, and let δ=d(q,pn1). Suppose n1,,ni1 are chosen. Since q is a limit point of E, there is an xE with d(q,x)<δ2i. Since xE and is thus the limit (sequence) of some subsequence of {pn}, there is an ni>ni1 such that d(x,pni)<δ2i. Now, via the triangle inequality,

d(q,pni)d(q,x)+d(x,pni)<δ2i+δ2i=2δ2i=δ2i1,i=1,2,3,.

This means that {pni} converges to q, because we can find a pni as close as desired to q. Therefore q is a subsequential limit of {pn} and so qE, and E is closed.

Referenced by (1 direct)

Direct references:

Theorem: Sum and Difference of Cubes \@{theorem-1}

Sum of cubes:

a3+b3=(a+b)(a2ab+b2)

Difference of cubes:

a3b3=(ab)(a2+ab+b2)

Theorem: Sum of Independent Normal Random Variables \@{sum-independent-normal}

If X1,X2,Xn are independent normal random variables with means μ1,,μn and variances σ12,,σn2, then the random variable X1+X2++Xn is normal with mean μ1+μ2++μn and variance σ12+σ22++σn2.

Theorem: Sum of Independent Poisson Random Variables \@{sum-independent-poisson}

If X1,X2,Xn are independent Poisson random variables with parameters λ1,λ2,,λn, then X1+X2++Xn is a Poisson random variable with parameter λ1+λ2++λn.

Theorem \@{sums-and-products-of-sequences}

Suppose {sn} and {tn} are complex sequences, and limnsn=s,limntn=t. Then

(a) limn(sn+tn)=s+t;

(b) limncsn=cs, for any number c;

(c) limn(c+sn)=c+s, for any number c;

(d) limnsntn=st;

(e) limn1sn=1s,sn0,s0.

Proof \@{proof-of-sums-and-products-of-sequences}

For (a), let ϵ>0, pick Ns such that |sns|<ϵ/2 when nNs, and pick Nt such that |tns|<ϵ/2 when nNt. Then, let N=max{Ns,Nt}. Then, when nN,

|(sn+tn)(s+t)|=|(sns)+(tnt)||sns|+|tnt|<ϵ.

For (b), If c=0, let ϵ>0, and then |csncs|=0<ϵ. Otherwise,

Let ϵ/|c|>0. For some N, when nN, we have

|sns|<ϵ/|c||c||sns|<ϵ|csncs|<ϵ.

For (c), let ϵ>0. For some N, when nN, we have

|(sn+c)(s+c)|=|sns|<ϵ.

For (d), first note the identity

sntnst=(sns)(tnt)+s(tnt)+t(sns).(1)

Now, let ϵ>0. Pick Ns such that |sns|<ϵ when nNs, and pick Nt such that |tns|<ϵ when nNt. Then, let N=max{Ns,Nt}. Then, when nN,

|(sns)(tnt)|<ϵ,

which means

limn(sns)(tnt)=0.

Applying this, along with the results of (a) and (b) to (1) gives:

limn(sntnst)=limn((sns)(tnt)+s(tnt)+t(sns))=limn(s(tnt)+t(sns))=limn(s(tnt))+limn(t(sns))=s0+t0=0.

For (e), pick m such that when nm,|sns|<12|s|, so we have that

|sns|<12|s||sn|+|s|<12|s||sn|<12|s||sn|>12|s|12|sn||s|<1.

Now, let ϵ>0. For some integer N>m, when nM, we have that

|sns|<12|s|2ϵ.

Thus, when nN,

|1sn1s|=|snssns|=|sns||sns|<12|sn||s||s|2ϵ=12|sn||s|ϵ<ϵ.

Referenced by (1 direct)

Direct references:

Theorem \@{sup-is-in-closure-of-bounded-nonempty-set-of-reals}

Let E be a nonempty set of real numbers which is bounded above. Let y=supE. Then yE. Hence yE if E is closed.

Proof \@{proof-of-sup-is-in-closure-of-bounded-nonempty-set-of-reals}

Suppose yE. Then yE. Suppose yE. Now, by hypothesis, for every h>0, there is some x such that yh<x<y, because otherwise, x would be an upper bound on E. Therefore, every neighborhood Nh(y) contains some xE, and thus y is a limit point of E and yE.

Referenced by (1 direct)
Theorem: Taylor Expansion Theorem \@{taylor-expansion-theorem}

Let f be analytic in a domain D and z0 be a point in D. Then f can be expanded in a power series

f(z)=f(z0)+f(z0)(zz0)+f(z0)2!(zz0)2+,

valid in all circles zz0<r containing only points of D.

Theorem \@{theorem-1}

If a function f(z) can be expressed in the form

f(z)=(zz0)mg(z)

valid in some circle zz0<R, where g(z) is analytic at z0 and g(z0)0, then f(z) has a zero of order m at z0.

Intuition \@{theorem-1-intuition}

We can see this from the definition of the taylor series. Assume we're at a zero, that is, that f(z0)=0. Then

f(z)=f(z0)+f(z0)(zz0)+f(z0)2!(zz0)2+,

and since f(z0) is 0, we're left with all terms that have (zz0) in them, and so we can factor them out and get

f(z)=(zz0)(f(z0)+f(z0)2!(zz0)+f(z0)3!(zz0)2+.

Now, if f(z0) does not have a zero at z0, then f(z0) is not zero and f(z) has a first order zero at z0. If f(z0) has a zero at z0, we can repeat and move onto the next term until, going through m terms, until we find a term that doesn't have a zero at z0, in which case we will have factored out (zz0)m and we'll be left with an analytic g(z) with g(z0)0.

from Zeros
Theorem \@{theorem-1}

02πabcsinθdθ=02πabccosθdθ=2πab2c2,b2>c2.

Theorem \@{theorem-1}

f^^(ω)=f(ω).

Proof \@{proof-of-theorem-1}

First we just write the definition: f^^(ω)=12π12πf(x)eikxdxeikωdk

Now we move the outer exponential into the inner integral, which is allowed since it's constant in x.:

f^^(ω)=12πf(x)eik(x+ω)dxdk

Now we swap the order if integration. We do this in calc 3... some justify it with Fubini's theorem, it's not clear exactly why this is allowed but this is how it goes (TODO: figure this out)

f^^(ω)=12πf(x)eik(x+ω)dkdx

Now pull out f(x) from the inner integral since it's a constant in k:

f^^(ω)=12πf(x)eik(x+ω)dkdx

The inner integral is just a delta function:

f^^(ω)=12πf(x)2πδ(x+ω).dx

Now by, the sifting property of the delta function, this just reduces to

f^^(ω)=f(ω).

Theorem \@{theorem-1}

The nth roots of unity under multiplication are isomorphic to Zn under addition.

Proof \@{proof-of-theorem-1}

Let

μn={e2piik/n:k=0,1,,n1}

be the nth roots of unity, and

Zn={0,1,,n1}

be the first n non-negative integers under addition modulo n.

Let

ϕ:Znμn,ϕ(k)=e2πik/n

be a mapping we will show to be an isomorphism.

First, note that μn and Zn are obviously the same cardinality, by their definitions.

Now, suppose a,b<n1. Then

ϕ(a+b)=e2πi(a+b)/n=e2πia/ne2πib/n=ϕ(a)ϕ(b),

so ϕ is a @group-homomorphism.

Now, eμn is 1, that is, e2πi(0)/n, so kerϕ=0. But, 0 is eZn, and so ϕ is @injective, and an @isomorphism, and μn and Zn are isomorphic.

Theorem \@{theorem-12}

The only H satisfying the three required properties above is the entropy function defined above, up to multiplication by a constant.

Proof \@{proof-of-theorem-12}

Assume we have a function A(n) that satisfies the three properties listed above, such that

A(n)=H(1n,1n,,1n).

Then, by property (3) above, we can decompose a choice from sm equally likely possibilities into a sequence of m choices each from s equally likely possibilities. For example, if we have 24 equally likely possibilities, the probability of any given event is 1/16. If we instead we have a series of 4 choices each with 1/2 probability, we end up with 1/16 as the probability of any specific sequence of events. So, we have that A(sm)=mA(s).

Now, with arbitrarily large n, we can also have tn such that A(tn)=nA(t), by the same logic, and we can pick m such that

smtn<sm+1.

Now, we can take the logarithm of each term to get

mlogsnlogt<(m+1)logs,

and dividing by nlogs gives

mnlogtlogs<mn+1n,

and because n is arbitrarily large,

|mnlogtlogs|<ϵ,

where ϵ is arbitrarily small.

By property (2) of A(n) (it is a @monotonically-increasing function of n,)

A(sm)A(tn)A(sm+1)mA(s)nA(t)(m+1)A(s).

Then, dividing by nA(s) gives

mnA(t)A(s)mn+1n or |mnA(t)A(s)|<ϵ,

Now, by the @triangle-inequality, we have that

|A(t)A(s)logtlogs|2ϵ|A(t)A(s)logslogt|2ϵA(s).

Since ϵ can be arbitrarily small, we have that A(t)=Klog(t), with K>0 so that property (2) holds. Now we know what A(n) is, and thus what H is when we have equal probabilities for all events.

Now let's say that we have a choice from n possible events with commensurable probabilities pi=nini. We can break down a choice from ni possibilities into a choice from n possibilities with probabilities p1,,pn and then, if the ith possibility was chosen, ni choices of equal probability pi. We do this because above, we found how to find H when all events are equally likely, and property (3) of our desired function lets us break down our overall choice from ni possibilities. This gives us

Klogni=H(p1,,pn)+piA(ni)=H(p1,,pn)+piKlogni=H(p1,,pn)+Kpilogni.

Then, H(p1,,pn)=KlogniKpilogni=K[lognipilogni]=K[pi(logni)pi(logni)] because pi=1=K[pi(lognilogni)]=K[pi(lognini)]=Kpilogpi.

If the pi are incommensurable, we can approximate them as closely as we'd like with rationals, since The rationals are dense in the reals.. By the first property we assumed for H, it is continuous in the pi, and so its value at the incommensurable pi equals its limit as we approach via the rationals, and so our expression holds in general. K is left to us to pick, picking it is equivalent to picking a base for the logarithm.

Theorem \@{theorem-15}

When a random variable is uniformly distributed over an alphabet of n elements, the self-information of any given element equals the entropy of the random variable and is logn.

Theorem \@{theorem-16}

H=0 iff all the pi but one are zero, this one having the value of one.

Proof \@{proof-of-theorem-16}

Suppose H(p1,,pn)=0. Then, 0=i=1npilogpi. Note that because 0pi1, we have logpi0, and pilogpi0 (with the convention 0log0=0, since limp0+plogp=0). Assume for contradiction that more than one pi is non-zero. Then, because pi=1, each non-zero pi is in (0,1) and therefore its pilogpi>0, and the sum of these terms is therefore non-zero, a contradiction. Now, since all probabilities are required to sum to 1, it can't be the case that all probabilities are zero, which means that exactly one probability must be non-zero and that probability must be 1.

Theorem \@{theorem-17}

If a system has a liapunov function, then its fixed point x is globally asymptotically stable: for all initial conditions, x(t)x as t. Therefore, the system has no closed orbits.

Intuition \@{theorem-17-intuition}

Like gradient systems, we can't get in a loop if we're always moving downhill.

Theorem \@{theorem-18}

A random variable has the most entropy when the elements of its alphabet are all equally likely to occur.

Proof \@{proof-of-theorem-18}

We will use a @Lagrangian function of entropy to show this. Let

L(p,λ)=i[pilnpi]λ[(ipi)1].

Then, for any pi we want

Lpi=lnpi1λ=0pi=e1λ.

But, this means pi doesn't depend on i, and so is the same for each i and therefore pi=1n.

It remains to show that this extreme of H is a maximum. This follows from the facts that the domain is a @convex, compact set and that the entropy function is strictly @concave. TODO: more details on these.

Theorem \@{theorem-19}

Let f(P)=f(x,y,z) be a scalar function having continuous first partial derivatives in some domain B in space. Then, gradf exists in B and is a vector, that is, its length and direction are independent of the particular choice of Cartesian coordinates. If gradf(P)0 at some point P, it has the direction of maximum increase of f at P.

Proof \@{proof-of-theorem-19}

The directional derivative of f in the direction of some unit vector u is

Duf=ugradf=|u||gradf|cosθ(a)

where θ is the angle between u and gradf (see The directional derivative of f in the... and The dot product of u and v...). Note that f is a scalar function, as is the directional derivative of f. Now, cosθ has its maximum value of 1 whenever θ=0, and since u is a unit vector with magnitude of 1, (a) simplifies to

Duf=|gradf|,

which tells us that that direction and magnitude of gradf are independent of the coordinate system chosen. Now, since θ=0 if and only if b and gradf are parallel, gradf is the direction of maximum increase of f at P, assuming gradf0 at P.

Theorem \@{theorem-19}

Let x˙=f(x) be a continuously differentiable vector field defined on a @simply-connected subset R of the @plane. If there exists a continuously differentiable, @real-valued function g(x) such that (gx˙) has one sign throughout R, then there are no closed orbits lying entirely in R.

Theorem \@{theorem-23}

A sequence {pn} converges to p if and only if every subsequence of {pn} converges to p.

Proof \@{proof-of-theorem-23}

Suppose that {pn} converges to p. Suppose some subsequence {pni} converges to q, and suppose, for contradiction, that qp. Now, following an argument similar to the proof that limits of sequences are unique, we can see that if pq, arbitrary neighborhoods around both can't contain all but finitely many points, so we have a contradiction, and p=q.

Conversely, suppose every subsequence of {pn} converges to p. Then, {pn} is a subsequence of itself, so it converges to p.

Theorem \@{theorem-3}

pjk(m)=Pjkm.

Proof \@{proof-of-theorem-3}

Due to the magic of linear algebra, if we have a row vector x representing the probability of starting in each state, we can compute the probability of ending up in any given state after m steps as

xPm.

Theorem \@{theorem-36}

The uncertainty of Y is never increased by knowledge of X. It will be decreased unless X and Y are independent events, in which case it is not changed.

Proof \@{proof-of-theorem-36}

From Let X and Y be discrete random... and Chain rule for joint entropy, we have

H(X)+H(Y)H(X,Y)=H(X)+H(Y|X),

hence H(Y)H(Y|X).

Theorem \@{theorem-5}

Let bij(s) be the duration of the s-th symbol which is allowable in state i and leads to state j. Then the channel-capacity C is equal to logW where W is the largest real root of the @determinantal-equation

|sWbij(s)δij|=0,

where δij is the @Kronecker-delta.

Theorem \@{theorem-50}

Every closed subset E of a complete metric space X is complete.

Proof \@{proof-of-theorem-50}

Let {pn}E be a cauchy sequence. Then, it converges to some point pX, and actually pE, because E is closed. Therefore, E is complete.

Theorem \@{theorem-6}

Let C be a simple, closed piecewise smooth curve, and C1, C2, , Cn be disjoint, simple, closed piecewise smooth curves in the interior of C. If f is analytic at all points that are both inside or on C, and outside or on each Cj, then

cf(z)dz=j=1nCjf(z)dz.

Theorem \@{theorem-7}

Suppose EX, a metric space, p is a limit point of E, f and g are complex functions on E, and

limxpf(x)=A,limxpg(x)=B.

Then:

limxp(f+g)(x)=A+B.

limxp(fg)(x)=AB.

limxp(fg)(x)=AB,B0.

Proof \@{proof-of-theorem-7}
Remark \@{theorem-7-remark}

If f and g map E into Rk, then (a) remains true, and (b) becomes

limxp(fg)(x)=AB.

That is, the limit of an inner product of vector valued functions is the inner product of their limits.

(See A sequence in Rk converges iff its components converge).

Theorem \@{theorem-7}

If C is a simple, closed, piecewise smooth curve and z0 is interior to C, then:

C1zz0dz=2πi.

Intuition \@{theorem-7-intuition}

See If C is the unit circle, then... and consider path independence and the principle of path deformation. Consider toy contours, particularly a collapsed keyhole contour around the singularity.

Theorem \@{theorem-7}

Every power series representation of (or, Taylor series for) an entire-function has an infinite radius of convergence.

Theorem \@{theorem-7}
Theorem \@{theorem-9}

If f(1/z) has a singularity at 0, then f(z) has a singularity at the @point-at-infinity.

Theorem: Total derivatives are unique \@{total-derivatives-are-unique}

Suppose E is an open set in Rn, f:ERm, and xE, and that A1 and A2 are total derivatives of f at x. Then, A1=A2.

Theorem \@{union-and-intersection-of-open-and-closed-sets}

(a) - For any collection {Ga} of open sets, αGα is open.

(b) - For any collection {Fa} of closed sets, αFα is closed.

(c) - For any finite collection G1,,Gn of open sets, i=1nGi is open.

(d) - For any finite collection F1,,Fn of closed sets, i=1nFi is closed.

Proof \@{proof-of-union-and-intersection-of-open-and-closed-sets}

Let G=αGα,xG. Then x is in some Gα for some α, and is an interior point of that Gα, since Gα is open. Therefore, x has some neighborhood that is a subset of Gα and therefore of G, so x is an interior point of G and G is open - this shows (a).

Note that

(αFα)c=αFαc,(e)

and by (a) above, (e) is open. Then its complement, αFα is closed, and we've shown (b).

Now, let x be in G=i=1nGi, so x is in every Gi, and has a neighborhood Ni in every Gi with radius ri>0. Let r be min{r1,,rn}. Then, x has a neighborhood N of radius r in every Gi, and thus in G, so x is an interior point of G, and we've shown (c).

Now, (i=1nFi)c=i=1nFic is open by (c), so its complement, i=1nFi is closed, and we've shown (d).

Note \@{union-and-intersection-of-open-and-closed-sets-note}

In parts (c) and (d) of the above theorem, finiteness of the collections of sets is required - the property do not necessarily hold for infinite collections of sets.

Theorem \@{union-of-a-sequence-of-countable-sets-is-countable}

Let {En},n=1,2,3, be a sequence of countable sets. Then let S=n=1En. Then, S is countable.

Proof \@{proof-of-union-of-a-sequence-of-countable-sets-is-countable}

We can construct an infinite array where the rows are sequence constructed by the sets that make up the entries of {En}. Then, we can create a single sequence from all the entries of the sets of {En} by iterating over them in the following order:

sequence = []
for i in range(1, k):
    for j in range(0, i):
        n = i - j
        m = j + 1
        sequence.append(f"E_{n},{m}")

Which, for k=5, yields

E1,1,E2,1,E1,2,E3,1,E2,2,E1,3,E4,1,E3,2,E2,3,E1,4.

This sequence may contain duplicates, so some indices may need to be skipped in constructing a subset T of the positive integers such that T S, but we've now shown that S is at most countable. To show S is infinite and therefore countable, note that the infinite set E1 is a subset of S,, and therefore S is infinite and countable.

Referenced by (1 direct)

Start with a source alphabet with n symbols a1,,an, emitted @i.i.d. with probabilities p1,,pn (so pi=Pr(ai)). Its entropy is, by definition,

H=i=1npilogpi.

A message is a sequence S=(x1,,xN) of length N, each xj{a1,,an}. By independence,

pS=j=1NPr(xj)

is the probability of the sequence S being emitted.

Let ϵ,δ>0. Then, there exists N0 such that for all NN0, the length-N sequences split into two classes:

  1. The typical set, Aδ(N)={S:|1Nlog1pSH|δ}.
  1. The atypical set, Aδ(N), with total probability Pr(Aδ(N))<ϵ.

Equivalently, Pr(Aδ(N))1 as N, i.e. 1Nlog1pSH in probability.

Proof \@{proof-of-theorem-2}

First some preliminaries. We have two indices to keep straight here:

  • n - our alphabet size; we use i{1,,n} to index distinct symbols ai, each with its own probability pi.
  • N - sequence length; we use j{1,,N} to index positions in a sequence, each holding some symbol xj.

If we let ci=#{j:xj=ai} be the count of symbol ai in S, so i=1nci=N, then we can restate pS as

pS=i=1npici.

ci is the only random thing in pS - it doesn't consider position of symbols, just the total count of each symbol (random) and the probability of that symbol being emitted on any given turn (fixed).

Taking advantage of our symbols being emitted @i.i.d., we can convert our expression about the surprisal of the sequence to an equivalent expression about the surprisal of individual symbols being emitted:

1Nlog1pS=1Nlog1i=1npici=1Nlogi=1npici=1Ni=1nlogpici=i=1nciNlogpi.

This final term is very similar to H=i=1npilogpi; we just need to show that ciNpi as N. The expected value of ci for N draws is the expected count of ai in N draws, and is

E(ci)=Npi.

So, by the @weak-law-of-large-numbers, ciNpi as N. That is, 1Nlog1pS=i=1nciNlogpi @converges-in-probability to H as N. This means that for any δ>0,

Pr(|1Nlog1pSH|>δ)N0.

That is, that for every ϵ>0, there exists N0 such that for all NN0,

Pr(|1Nlog1pSH|>δ)<ϵ.

That is to say, that the total probability of getting a sequence where the surprisal rate 1Nlog1pS is more than δ from H is less than ϵ for all N larger than some N0, which is what we wanted to show.

Intuition \@{theorem-2-intuition}

This is just saying that the empirical frequencies in sequences of symbols generated by this source, (c1N,,cnN) approach the true probabilities for the source distribution, p1,,pn as N. In other words, the sequences most likely to be produced by the source (typical sequences) are all about equally likely to occur, with probability

pS2HN.

Theorem: Weierstrass \@{weierstrass}

Every bounded infinite subset of Rk has a limit point in Rk.

Proof \@{proof-of-weierstrass}

Suppose E is a bounded infinite subset of Rk. Then, it is a subset of a k-cell IRk, and because every k-cell is compact, I is compact. Since infinite subsets of a compact set K have a limit point in K, E has a limit point in I and therefore in Rk.

Note \@{weierstrass-note}

This theorem shows up in other forms, especially related to sequences. For example, in my intro real analysis class, it was expressed as the much weaker "Every bounded sequence in R1 has a convergent subsequence." Other equivalent forms are

  • Any bounded sequence in Rk has a convergent subsequence.
  • Closed and bounded subsets of Rk are sequentially compact.

There seem to be two approaches to topology of metric spaces - the point/set approach used by Rudin and covered here, and a sequence based approach that many other authors like Pugh use in introductory texts. We don't use the term "sequentially compact" anywhere in this page - that's work for a future exercise.

Referenced by (1 direct)