Publicação
A filter inexact-restoration method for nonlinear programming
| Resumo: | A new iterative algorithm based on the inexact-restoration (IR) approach combined with the filter strategy to solve nonlinear constrained optimization problems is presented. The high level algorithm is suggested by Gonzaga et al. [7] but not yet implemented - the internal algorithms are not proposed. The filter, a new concept introduced by Fletcher and Leyffer [3], replaces the merit function avoiding the penalty parameter estimation and the difficulties related to the nondifferentiability. In the IR approach two independent phases are performed in each iteration - the feasibility and the optimality phases. The line search filter is combined with the first one phase to generate a “more feasible” point and then it is used in the optimality phase to reach an “optimal” point. Numerical experiences with a collection of AMPL problems and a performance comparison with IPOPT are provided. |
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| Autores principais: | Monteiro, M. Teresa T. |
| Outros Autores: | Silva, Cândida |
| Assunto: | Filter method Inexact restoration Line search |
| Ano: | 2008 |
| País: | Portugal |
| Tipo de documento: | artigo |
| Tipo de acesso: | acesso aberto |
| Instituição associada: | Universidade do Minho |
| Idioma: | inglês |
| Origem: | RepositóriUM - Universidade do Minho |
| Resumo: | A new iterative algorithm based on the inexact-restoration (IR) approach combined with the filter strategy to solve nonlinear constrained optimization problems is presented. The high level algorithm is suggested by Gonzaga et al. [7] but not yet implemented - the internal algorithms are not proposed. The filter, a new concept introduced by Fletcher and Leyffer [3], replaces the merit function avoiding the penalty parameter estimation and the difficulties related to the nondifferentiability. In the IR approach two independent phases are performed in each iteration - the feasibility and the optimality phases. The line search filter is combined with the first one phase to generate a “more feasible” point and then it is used in the optimality phase to reach an “optimal” point. Numerical experiences with a collection of AMPL problems and a performance comparison with IPOPT are provided. |
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