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A structured diagonal Hessian approximation method with evaluation complexity analysis for nonlinear least squares

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Autor(es):
Mohammad, Hassan ; Santos, Sandra A.
Número total de Autores: 2
Tipo de documento: Artigo Científico
Fonte: COMPUTATIONAL & APPLIED MATHEMATICS; v. 37, n. 5, p. 35-pg., 2018-11-01.
Resumo

This work proposes a Jacobian-free strategy for addressing large-scale nonlinear least-squares problems, in which structured secant conditions are used to define a diagonal approximation for the Hessian matrix. Proper safeguards are devised to ensure descent directions along the generated sequence. Worst-case evaluation analysis is provided within the framework of a non-monotone line search. Numerical experiments contextualize the proposed strategy, by addressing structured problems from the literature, also solved by related and recently presented conjugate gradient and multivariate spectral gradient strategies, as well as the classic Fletcher-Reeves conjugate gradient, and the Raydan-Barzilai-Borwein methods. The comparative computational results show a favorable performance of the proposed approach, mainly as far as robustness is concerned. (AU)

Processo FAPESP: 13/07375-0 - CeMEAI - Centro de Ciências Matemáticas Aplicadas à Indústria
Beneficiário:Francisco Louzada Neto
Modalidade de apoio: Auxílio à Pesquisa - Centros de Pesquisa, Inovação e Difusão - CEPIDs
Processo FAPESP: 13/05475-7 - Métodos computacionais de otimização
Beneficiário:Sandra Augusta Santos
Modalidade de apoio: Auxílio à Pesquisa - Temático