A convergent hierarchy of SDP relaxations for a class of hard robust global polynomial optimization problems

被引：3
|
作者：
Chieu, N. H. [1 ,2 ]
Jeyakumar, V. [1 ]
Li, G. [1 ]
机构：
[1] Univ New South Wales, Dept Appl Math, Sydney, NSW 2052, Australia
[2] Vinh Univ, Inst Nat Sci Educ, Vinh, Nghe An, Vietnam
来源：
OPERATIONS RESEARCH LETTERS | 2017年 / 45卷 / 04期
基金：
澳大利亚研究理事会;
关键词：
Robust optimization; Global polynomial optimization; Optimization under data uncertainty; Nonconvex optimization; Semi-definite programming relaxations; SQUARES RELAXATIONS; PROGRAMS;
D O I：
10.1016/j.orl.2017.04.005
中图分类号：
C93 [管理学]; O22 [运筹学];
学科分类号：
070105 ; 12 ; 1201 ; 1202 ; 120202 ;
摘要：
A hierarchy of semidefinite programming (SDP) relaxations is proposed for solving a broad class of hard nonconvex robust polynomial optimization problems under constraint data uncertainty, described by convex quadratic inequalities. This class of robust polynomial optimization problems, in general, does not admit exact semidefinite program reformulations. Convergence of the proposed SDP hierarchy is given under suitable and easily verifiable conditions. Known exact relaxation results are also deduced from the proposed scheme for the special class of robust convex quadratic programs. Numerical examples are provided, demonstrating the results. (C) 2017 Elsevier B.V. All rights reserved.
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页码：325 / 333
页数：9
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