lacunary - Mathnotes

Convexity

Definition: Strictly Convex Function \@{strictly-convex-function}

Starting with convex function, φ is strictly convex if the inequality is strict whenever x≠y.

Theorem: Adding an Affine Function Preserves Convexity \@{adding-an-affine-function-preserves-convexity}

Let E⊆Rk be a convex set, let φ:E→R, and let ℓ:E→R be affine. If φ is convex, then φ+ℓ and φ−ℓ are convex. If φ is concave, then φ+ℓ and φ−ℓ are concave. A linear function is affine, so this covers adding or subtracting a linear function.

TODO: prove this.

Referenced by (1 direct)
Theorem: Affine Precomposition Preserves Convexity \@{affine-precomposition-preserves-convexity}

Let D⊆Rk and E⊆Rm be convex sets, let A:D→E be affine, and let φ:E→R. If φ is convex, then φ∘A is convex on D. If φ is concave, then φ∘A is concave on D.

TODO: prove this.

Referenced by (1 direct)
Theorem: Second Derivative Test for Convexity \@{second-derivative-test-for-convexity}

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).

Referenced by (1 direct)

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Note \@{note-8}

Every convex combination of two points lies on the @line-segment between the points.