Convergence of the proximal bundle algorithm for nonsmooth nonconvex optimization problems

Autor:	S. Nobakhtian, N. Hoseini Monjezi
Rok vydání:	2021
Předmět:	Control and Optimization Quadratic equation Optimization problem Computer science Bundle Convergence (routing) Point (geometry) Computational intelligence Lipschitz continuity MATLAB computer Algorithm computer.programming_language
Zdroj:	Optimization Letters. 16:1495-1511
ISSN:	1862-4480 1862-4472
DOI:	10.1007/s11590-021-01787-0
Popis:	A proximal bundle algorithm is proposed for solving unconstrained nonsmooth nonconvex optimization problems. At each iteration, using already generated information, the algorithm defines a convex model of the augmented objective function. Then by solving a quadratic subproblem a new candidate iterate is obtained and the algorithm is repeated. The novelty in our approach is that the objective function can be any arbitrary locally Lipschitz function without any additional assumptions. The global convergence, starting from any point, is also studied. At the end, some encouraging numerical results with a MATLAB implementation are reported.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::4555356f54064db1d3270aedee8f780a https://doi.org/10.1007/s11590-021-01787-0 Zobrazit plný text záznamu Full text from SpringerLink