Constrained Consensus-Based Optimization

Giacomo Borghi, Michael Herty, Lorenzo Pareschi

Research output: Contribution to journalArticlepeer-review

5 Citations (Scopus)

Abstract

In this work we are interested in the construction of numerical methods for high-dimensional constrained nonlinear optimization problems by particle-based gradient-free techniques. A consensus-based optimization (CBO) approach combined with suitable penalization techniques is introduced for this purpose. The method relies on a reformulation of the constrained minimization problem in an unconstrained problem for a penalty function and extends to the constrained settings of the class of CBO methods. Exact penalization is employed and, since the optimal penalty parameter is unknown, an iterative strategy is proposed that successively updates the parameter based on the constrained violation. Using a mean-field description of the many particle limit of the arising CBO dynamics, we are able to show convergence of the proposed method to the minimum for general nonlinear constrained problems. Properties of the new algorithm are analyzed. Several numerical examples, also in high dimensions, illustrate the theoretical findings and the good performance of the new numerical method.

Original languageEnglish
Pages (from-to)211-236
Number of pages26
JournalSIAM Journal on Optimization
Volume33
Issue number1
Early online date28 Jan 2023
DOIs
Publication statusPublished - Mar 2023

Keywords

  • consensus-based optimization
  • constrained nonlinear minimization
  • gradient-free methods
  • mean-field limit

ASJC Scopus subject areas

  • Software
  • Theoretical Computer Science
  • Applied Mathematics

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