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Tight relaxations for polynomial optimization

2018-12-14 16:00

报告人: Nie Jiawang 【 UCSD】

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时间: 2018-12-14 16:00-17:00

地点: 卫津路校区14-202

开始时间: 2018-12-14 16:00-17:00

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报告人简介

UCSD

报告内容介绍

We propose tight semidefinite relaxations for polynomial optimization. The optimality conditions are investigated. We show that generally Lagrange multipliers can be expressed as polynomial functions in decision variables over the set of critical points. The polynomial expressions can be determined by linear equations. Based on these expressions, new Lasserre type semidefinite relaxations are constructed for solving polynomial optimization. We show that the hierarchy of new relaxations has finite convergence, or equivalently, the new relaxations are tight for a finite relaxation order.

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Tight relaxations for polynomial optimization

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报告内容介绍