Line Search and Trust-Region Methods for Convex-Composite Optimization

Abstract

We consider descent methods for solving non-finite valued nonsmooth convex-composite optimization problems that employ Gauss-Newton subproblems to determine the iteration update. Specifically, we establish the global convergence properties for descent methods that use a backtracking line search, a weak Wolfe line search, or a trust-region update. All of these approaches are designed to exploit the structure associated with convex-composite problems.

References in corpus (2)

• Solving (most) of a set of quadratic equalities: Composite optimization for robust phase retrieval
• Strong Metric (Sub)regularity of KKT Mappings for Piecewise Linear-Quadratic Convex-Composite Optimization

Cited by in corpus (2)

• Stochastic Variance-Reduced Prox-Linear Algorithms for Nonconvex Composite Optimization
• A Trust-Region Method For Nonsmooth Nonconvex Optimization