MATRS: heuristic methods for noisy derivative-free bound-constrained mixed-integer optimization

  • This paper introduces MATRS, a novel matrix adaptation trust-region strategy designed to solve noisy derivative-free mixed-integer optimization problems with simple bounds in low dimensions. MATRS operates through a repeated cycle of five phases: mutation, selection, recombination, trust-region, and mixed-integer, executed in this sequence. But if in the mutation phase a new best point (the point with the lowest inexact function value among all evaluated points so far) is found, the selection, recombination, and trust-region phases are skipped. Similarly, if the recombination phase finds a new best point, the trust-region phase is skipped. The mixed-integer phase is always performed. To search for a new best point, the mutation and recombination phases use extrapolation whereas the mixed-integer phase performs a mixed-integer line search along directionsn estimated to go into a valley. Numerical results on several collections of test problems show that MATRS is competitive with state-of-the-art derivative-free mixedinteger solvers.

Download full text files

Export metadata

Additional Services

Share in Twitter Search Google Scholar
Metadaten
Author:Morteza Kimiaei, Arnold Neumaier
DOI:https://doi.org/10.1007/s12532-025-00281-3
ISSN:1867-2949
Parent Title (English):Mathematical Programming Computation
Publisher:Springer Science and Business Media LLC
Document Type:Article
Language:English
Year of Completion:2025
Volume:17
Issue:3
Page Number:42
First Page:505
Last Page:546
Mathematical Programming Computation :MPC 2025 - Issue 3
Verstanden ✔
Diese Webseite verwendet technisch erforderliche Session-Cookies. Durch die weitere Nutzung der Webseite stimmen Sie diesem zu. Unsere Datenschutzerklärung finden Sie hier.