A globally convergent method for nonlinear least-squares problems based on the Gauss-Newton model with spectral correction

Autor(es)

Goncalves, Douglas S. ^[1] ; Santos, Sandra A. ^[2]

Afiliação do(s) autor(es)

^[1] Univ Fed Santa Catarina, CCFM, Dept Math, BR-88040900 Florianopolis, SC - Brazil

^[2] Univ Estadual Campinas, IMECC, Dept Appl Math, BR-13083970 Campinas, SP - Brazil

• Tipo de documento: Artigo Científico
• Fonte: BULLETIN OF COMPUTATIONAL APPLIED MATHEMATICS; v. 4, n. 2, p. 7-26, JUL-DEC 2016.
• Citações Web of Science: 1

Resumo

This work addresses a spectral correction for the Gauss-Newton model in the solution of nonlinear least-squares problems within a globally convergent algorithmic framework. The nonmonotone line search of Zhang and Hager is the chosen globalization tool. We show that the search directions obtained from the corrected Gauss-Newton model satisfy the conditions that ensure the global convergence under such a line search scheme. A numerical study assesses the impact of using the spectral correction for solving two sets of test problems from the literature. (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