×

A unified convergence analysis of block successive minimization methods for nonsmooth optimization. (English) Zbl 1273.90123

Summary: The block coordinate descent (BCD) method is widely used for minimizing a continuous function \(f\) of several block variables. At each iteration of this method, a single block of variables is optimized, while the remaining variables are held fixed. To ensure the convergence of the BCD method, the subproblem of each block variable needs to be solved to its unique global optimal. Unfortunately, this requirement is often too restrictive for many practical scenarios. In this paper, we study an alternative inexact BCD approach which updates the variable blocks by successively minimizing a sequence of approximations of \(f\) which are either locally tight upper bounds of \(f\) or strictly convex local approximations of \(f\). The main contributions of this work include the characterizations of the convergence conditions for a fairly wide class of such methods, especially for the cases where the objective functions are either nondifferentiable or nonconvex. Our results unify and extend the existing convergence results for many classical algorithms such as the BCD method, the difference of convex functions (DC) method, the expectation maximization (EM) algorithm, as well as the block forward-backward splitting algorithm, all of which are popular for large scale optimization problems involving big data.

MSC:

90C06 Large-scale problems in mathematical programming
90C26 Nonconvex programming, global optimization
90C55 Methods of successive quadratic programming type
94A05 Communication theory
93E10 Estimation and detection in stochastic control theory

Software:

UNLocBoX; AS 136
PDFBibTeX XMLCite
Full Text: DOI arXiv