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

DOI10.1016/j.orl.2017.04.005zbMath1409.90182OpenAlexW2611961450WikidataQ59241415 ScholiaQ59241415MaRDI QIDQ1728251

Guoyin Li, Vaithilingam Jeyakumar, Nguyen Huy Chieu

Publication date: 22 February 2019

Published in: Operations Research Letters (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.orl.2017.04.005

zbMATH Keywords

nonconvex optimizationrobust optimizationglobal polynomial optimizationoptimization under data uncertaintysemi-definite programming relaxations

Mathematics Subject Classification ID

Semidefinite programming (90C22) Nonlinear programming (90C30) Sensitivity, stability, parametric optimization (90C31)

Related Items (3)

Exact Second-Order Cone Programming Relaxations for Some Nonconvex Minimax Quadratic Optimization Problems ⋮ Convexifiability of continuous and discrete nonnegative quadratic programs for gap-free duality ⋮ Exact SDP reformulations of adjustable robust linear programs with box uncertainties under separable quadratic decision rules via SOS representations of non-negativity

Uses Software

• YALMIP
• SFSDP

Cites Work

• Unnamed Item
• Unnamed Item
• Semidefinite programming relaxation methods for global optimization problems with sparse polynomials and unbounded semialgebraic feasible sets
• On polynomial optimization over non-compact semi-algebraic sets
• An extension of sums of squares relaxations to polynomial optimization problems over symmetric cones
• Robust convex quadratically constrained programs
• Convergence of the Lasserre hierarchy of SDP relaxations for convex polynomial programs without compactness
• Exact SDP relaxations for classes of nonlinear semidefinite programming problems
• Robust SOS-convex polynomial optimization problems: exact SDP relaxations
• Matrix sum-of-squares relaxations for robust semi-definite programs
• Robust global optimization with polynomials
• Exploiting Sparsity in SDP Relaxation of Polynomial Optimization Problems
• Algorithm 920
• Constructing Uncertainty Sets for Robust Linear Optimization
• Theory and Applications of Robust Optimization
• Pre- and Post-Processing Sum-of-Squares Programs in Practice
• Convergent Relaxations of Polynomial Matrix Inequalities and Static Output Feedback

This page was built for publication: A convergent hierarchy of SDP relaxations for a class of hard robust global polynomial optimization problems