A bundle-filter method for nonsmooth convex constrained optimization

Authors:
Elizabeth Karas;Ademir Ribeiro;Claudia Sagastizábal;Mikhail Solodov
Affiliations:
Universidade Federal do Paraná, Departamento de Matemática, CP 19081, 81531-980, Curitiba, PR, Brazil;Universidade Federal do Paraná, Departamento de Matemática, CP 19081, 81531-980, Curitiba, PR, Brazil;Instituto de Matemática Pura e Aplicada, CP 19081, Estrada Dona Castorina 110, Jardim Botânico, 22460-320, Rio de Janeiro, RJ, Brazil;Instituto de Matemática Pura e Aplicada, CP 19081, Estrada Dona Castorina 110, Jardim Botânico, 22460-320, Rio de Janeiro, RJ, Brazil
Venue:
Mathematical Programming: Series A and B - Nonlinear convex optimization and variational inequalities
Year:
2008

Citing 0
Cited 3

Derivative-free nonlinear optimization filter simplex

International Journal of Applied Mathematics and Computer Science
An inexact spectral bundle method for convex quadratic semidefinite programming

Computational Optimization and Applications
Level bundle methods for constrained convex optimization with various oracles

Computational Optimization and Applications

Quantified Score

Hi-index	0.00

Visualization

Abstract

For solving nonsmooth convex constrained optimization problems, we propose an algorithm which combines the ideas of the proximal bundle methods with the filter strategy for evaluating candidate points. The resulting algorithm inherits some attractive features from both approaches. On the one hand, it allows effective control of the size of quadratic programming subproblems via the compression and aggregation techniques of proximal bundle methods. On the other hand, the filter criterion for accepting a candidate point as the new iterate is sometimes easier to satisfy than the usual descent condition in bundle methods. Some encouraging preliminary computational results are also reported.