Convex Optimization · Problem 10.3

Interior-point methods — Boyd & Vandenberghe

Mathematics — Optimization · dual-newton-method

Problem

Dual Newton method. In this problem we explore Newton's method for solving the dual of the equality constrained minimization problem (10.1). We assume that ff is twice differentiable, ∇2f(x)≻0\nabla^2 f(x) \succ 0 for all x∈dom fx \in \mathbf{dom}\, f, and that for each ν∈Rp\nu \in \mathbf{R}^p, the Lagrangian L(x,ν)=f(x)+νT(Ax−b)L(x, \nu) = f(x) + \nu^T(Ax - b) has a unique minimizer, which we denote x(ν)x(\nu). ((a)) Show that the dual function gg is twice differentiable. Find an expression for the Newton step for the dual function gg, evaluated at ν\nu, in terms of ff, ∇f\nabla f, and ∇2f\nabla^2 f, evaluated at x=x(ν)x = x(\nu). You can use the results of exercise 3.40. ((b)) Suppose there exists a KK such that ∥[∇2f(x)ATA0]−1∥2≤K\left\| \begin{bmatrix} \nabla^2 f(x) & A^T \\ A & 0 \end{bmatrix}^{-1} \right\|_2 \leq K for all x∈dom fx \in \mathbf{dom}\, f. Show that gg is strongly concave, with ∇2g(ν)⪯−(1/K)I\nabla^2 g(\nu) \preceq -(1/K)I.

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