A proximal method for composite minimization
A.S. Lewis (aslewisorie.cornell.edu)
Abstract: We consider minimization of functions that are compositions of convex or prox-regular functions (possibly extended-valued) with smooth vector functions. A wide variety of important optimization problems fall into this framework. We describe an algorithmic framework based on a subproblem constructed from a linearized approximation to the objective and a regularization term. Properties of local solutions of this subproblem underlie both a global convergence result and an identification property of the active manifold containing the solution of the original problem. Preliminary computational results on both convex and nonconvex examples are promising.
Keywords: prox-regular functions, polyhedral convex functions, sparse optimization, global convergence, active constraint identification
Category 1: Nonlinear Optimization (Constrained Nonlinear Optimization )
Category 2: Convex and Nonsmooth Optimization (Nonsmooth Optimization )
Citation: School of ORIE, Cornell University. Computer Sciences Department, University of Wisconsin.
Entry Submitted: 12/01/2008
Modify/Update this entry
|Visitors||Authors||More about us||Links|
Search, Browse the Repository
Give us feedback
|Optimization Journals, Sites, Societies|