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A Smoothing SQP Framework for a Class of Composite $L_q$ Minimization over Polyhedron

Ya-Feng Liu(yafliu***at***lsec.cc.ac.cn)
Shiqian Ma(sqma***at***se.cuhk.edu.hk)
Yu-Hong Dai(dyh***at***lsec.cc.ac.cn)
Shuzhong Zhang(zhangs***at***umn.edu)

Abstract: The composite $L_q$ (0<q<1) minimization problem over a general polyhedron has received various applications in machine learning, wireless communications, image restoration, signal reconstruction, etc. This paper aims to provide a theoretical study on this problem. Firstly, we show that for any fixed 0<q<1, finding the global minimizer of the problem, even its unconstrained counterpart, is strongly NP-hard. Secondly, we derive Karush-Kuhn-Tucker optimality conditions for local minimizers of the problem. Thirdly, we propose a smoothing sequential quadratic programming framework for solving this problem. The framework requires an (approximate) solution of a convex quadratic program at each iteration. Finally, we analyze the worst-case iteration complexity of the framework for returning an $\epsilon$-KKT point; i.e., a feasible point that satisfies a perturbed version of the derived KKT optimality conditions. To the best of our knowledge, the proposed framework is the first one with a worst-case iteration complexity guarantee for solving composite $L_q$ minimization over a general polyhedron.

Keywords: Composite $L_q$ minimization; nonsmooth nonconvex non-Lipschitzian optimization; worst-case iteration complexity

Category 1: Convex and Nonsmooth Optimization (Nonsmooth Optimization )

Category 2: Nonlinear Optimization (Constrained Nonlinear Optimization )

Citation:

Download: [PDF]

Entry Submitted: 07/28/2014
Entry Accepted: 07/28/2014
Entry Last Modified: 07/28/2014

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