Publicação
Método subgradiente incremental para otimização convexa não diferenciável
| Resumo: | We consider an optimization problem for which the objective function is the sum of convex functions, not necessarily differentiable. We study a subgradient method that executes the iterations incrementally selecting each component function sequentially and processing the subgradient iteration individually. We analyze different alternatives for choosing the step length, highlighting the convergence properties for each case. We also analyze the incremental model in other methods, considering proximal iteration and combinations of subgradient and proximal iterations. This incremental approach has been very successful when the number of component functions is large. |
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| Autores principais: | Adona, Vando Antônio |
| Assunto: | Método subgradiente incremental Otimização convexa Otimização não diferenciável Incremental subgradient method Convex optimization CIENCIAS EXATAS E DA TERRA::MATEMATICA |
| Ano: | 2015 |
| País: | Brasil |
| Tipo de documento: | dissertação de mestrado |
| Tipo de acesso: | unknown |
| Instituição associada: | IBICT |
| Idioma: | português |
| Origem: | Oasisbr |
| Resumo: | We consider an optimization problem for which the objective function is the sum of convex functions, not necessarily differentiable. We study a subgradient method that executes the iterations incrementally selecting each component function sequentially and processing the subgradient iteration individually. We analyze different alternatives for choosing the step length, highlighting the convergence properties for each case. We also analyze the incremental model in other methods, considering proximal iteration and combinations of subgradient and proximal iterations. This incremental approach has been very successful when the number of component functions is large. |
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