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A trust-region scheme for constrained multi-objective optimization problems with superlinear convergence property.
- Source :
-
Optimization Methods & Software . Jul2024, p1-41. 41p. 19 Illustrations. - Publication Year :
- 2024
-
Abstract
- In this paper, a numerical approximation method is developed to find approximate solutions to a class of constrained multi-objective optimization problems. All the functions of the problem are not necessarily convex functions. At each iteration of the method, a particular type of subproblem is solved using the trust region technique, and the step is evaluated using the notions of actual reduction and predicted reduction. A non-differentiable $ l_{\infty } $ l∞ penalty function restricts the constraint violations. An adaptive BFGS update formula is introduced. Global convergence of the proposed algorithm is established under the Mangasarian-Fromovitz constraint qualification and some mild assumptions. Furthermore, it is justified that the proposed algorithm displays a super-linear convergence rate. Numerical results are provided to show the efficiency of the algorithm in the quality of the approximated Pareto front. [ABSTRACT FROM AUTHOR]
- Subjects :
- *CONVEX functions
*CONSTRAINED optimization
*ALGORITHMS
Subjects
Details
- Language :
- English
- ISSN :
- 10556788
- Database :
- Academic Search Index
- Journal :
- Optimization Methods & Software
- Publication Type :
- Academic Journal
- Accession number :
- 178711329
- Full Text :
- https://doi.org/10.1080/10556788.2024.2372303