Convergent hierarchy of SDP relaxations for a class of semi-infinite convex polynomial programs and applications

作者：

Highlights：

• We present convergent hierarchy of semidefinite linear programming (SDP) relaxations.

• We formulate SDP relaxations via sum-of-squares polynomials and LMIs.

• We prove that the hierarchy exhibits finite convergence under additional conditions.

• We show applications to a broad class of robust polynomial optimization problems.

摘要

•We present convergent hierarchy of semidefinite linear programming (SDP) relaxations.•We formulate SDP relaxations via sum-of-squares polynomials and LMIs.•We prove that the hierarchy exhibits finite convergence under additional conditions.•We show applications to a broad class of robust polynomial optimization problems.

论文关键词：Semi-infinite optimization,Polynomial program,Semidefinite linear program,Relaxation problem,Approximation

论文评审过程：Received 26 November 2016, Revised 12 May 2017, Accepted 31 July 2017, Available online 12 September 2017, Version of Record 12 September 2017.

论文官网地址：https://doi.org/10.1016/j.amc.2017.07.076