Convex Function

Definition (Convex (891))

The function φ:(a,b)→R\varphi:(a,b)\to \mathbb{R} is convex on (a,b)(a,b) if and only if: ∀0≤λ≤1,∀a<x,y≤b\forall {0}\le \lambda\le 1,\forall a<x,y\le b: φ(λx+(1−λ)y)≤λφ(x)+(1−λ)φ(y)\varphi(\lambda x+(1-\lambda)y)\le \lambda \varphi (x)+(1-\lambda)\varphi(y) equivalently ∀a<s<t<u<b\forall a<s<t<u<b: φ(t)−φ(s)t−s≤φ(u)−φ(t)u−t\frac{\varphi(t)-\varphi(s)}{t-s}\le \frac{\varphi(u)-\varphi(t)}{u-t}

Definition (Convex (474))

The function f:K→Rf:K\to\mathbb{R}, where KK is a convex subset of Rn\mathbb{R}^n, is called convex on KK if ∀x1,x2∈K\forall x_1,x_2\in K and λ∈[0,1]\lambda\in[0,1] f(λx⃗1+(1−λ)x⃗2)≤λf(x⃗1)+(1−λ)f(x⃗2)f(\lambda\vec x_1+(1-\lambda)\vec x_2)\le\lambda f(\vec x_1)+(1-\lambda)f(\vec x_2) Also if strict inequality holds whenever x⃗1≠x⃗2\vec x_1\not=\vec x_2 and 0<λ<10<\lambda<1, then ff is called strictly convex.

Proposition (Properties of Convex Functions)

  1. Continuity: Convex functions are continuous on KK (except possibly the boundary of KK)
  2. Monotonicity of derivative equivalent to convexity: K⊂R,f∈C2  ⟹  (f convex  ⟺  f′′≥0)K\subset\mathbb{R}, f\in C^2\implies(f \text{ convex}\iff f''\ge0)
  3. If K⊂RnK\subset\mathbb{R}^n and ff is twice-differentiable in all its variables, then ff is convex if and only if it’s Hessian ∇2f=[δ2fδxiδxj]i,j\nabla^2 f=\left[\frac{\delta^2f}{\delta x_i\delta x_j}\right]_{i,j} is positive semidefinite.

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