Optimality conditions and global convergence for nonlinear semidefinite programming

• 成果类型: Article
• 署名作者: Andreani, Roberto; Haeser, Gabriel; Viana, Daiana S.
• 署名单位: Universidade Estadual de Campinas; Universidade de Sao Paulo; Universidade Federal do Acre (UFAC)
• 刊物名称: MATHEMATICAL PROGRAMMING
• ISSN/ISSBN: 0025-5610
• DOI: 10.1007/s10107-018-1354-5
• 发表日期: 2020
• 页码: 203-235
• 关键词: augmented lagrangian-methods; constraint qualifications; ROBUST-CONTROL; algorithm; optimization; INEQUALITY; proximity; points; 1st

摘要

Sequential optimality conditions have played a major role in unifying and extending global convergence results for several classes of algorithms for general nonlinear optimization. In this paper, we extend theses concepts for nonlinear semidefinite programming. We define two sequential optimality conditions for nonlinear semidefinite programming. The first is a natural extension of the so-called Approximate-Karush-Kuhn-Tucker (AKKT), well known in nonlinear optimization. The second one, called Trace-AKKT, is more natural in the context of semidefinite programming as the computation of eigenvalues is avoided. We propose an augmented Lagrangian algorithm that generates these types of sequences and new constraint qualifications are proposed, weaker than previously considered ones, which are sufficient for the global convergence of the algorithm to a stationary point.