Inexact Restoration approach for minimization with inexact evaluation of the objective function

DOI10.1090/mcom/3025zbMath1335.65053OpenAlexW2097738648WikidataQ113091023 ScholiaQ113091023MaRDI QIDQ2796018

Nataša Krejić, José Mario Martínez

Publication date: 23 March 2016

Published in: Mathematics of Computation (Search for Journal in Brave)

Full work available at URL: https://semanticscholar.org/paper/64d894bfcec239b4e58a96d014394521e5592d15

zbMATH Keywords

global convergenceconstrained optimizationnumerical experimentselectronic structure calculationslarge-scale problemsinexact restorationinexact evaluations

Mathematics Subject Classification ID

Numerical mathematical programming methods (65K05) Large-scale problems in mathematical programming (90C06) Applications of mathematical programming (90C90) Nonlinear programming (90C30)

Related Items

Inexact restoration for derivative-free expensive function minimization and applications, A Levenberg-Marquardt method for large nonlinear least-squares problems with dynamic accuracy in functions and gradients, Constrained optimization with integer and continuous variables using inexact restoration and projected gradients, On the computation of large-scale self-consistent-field iterations, Nonmonotone inexact restoration approach for minimization with orthogonality constraints, Inexact restoration for minimization with inexact evaluation both of the objective function and the constraints, Inexact restoration with subsampled trust-region methods for finite-sum minimization, Non-monotone inexact restoration method for nonlinear programming, On the employment of inexact restoration for the minimization of functions whose evaluation is subject to errors, Spectral projected gradient method for stochastic optimization, Iteration and evaluation complexity for the minimization of functions whose computation is intrinsically inexact, A stochastic first-order trust-region method with inexact restoration for finite-sum minimization, An inexact restoration-nonsmooth algorithm with variable accuracy for stochastic nonsmooth convex optimization problems in machine learning and stochastic linear complementarity problems

Uses Software

• AMLET

Cites Work

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