Seminário de Otimização & Problemas Inversos – 05/06/2023 às 14 horas

30/05/2023 11:07

Seminário de Otimização & Problemas Inversos

A Cubic Regularization of Newton Method with Finite-Difference Hessian Approximations

Max L. N. Gonçalves (UFG)

Resumo: In this talk, we present a version of the Cubic Regularization of the Newton method for unconstrained nonconvex optimization, in which the Hessian matrices are approximated by forward gradient differences. The regularization parameter of the cubic models and the accuracy of the Hessian approximations are jointly adjusted using a nonmonotone line-search criterion. Complexity analysis of the proposed algorithm is discussed and preliminary numerical experiments are presented to confirm our theoretical findings.

Palestrante: Max L. N. Gonçalves (UFG)
Data: Segunda-feira,  05 de Junho , 14h
Local: Auditório Airton Silva do Departamento de Matemática

Maiores informações:


E. Krukoski
Tags: Cubic RegularizationFinite-DifferenceHessian ApproximationsInversosMax L. N. GonçalvesNewton MethodotimizaçãoProblemasSeminario