Secant algorithms with nonmonotone trust region that employs fletcher penalty function for constrained optimization

DOI10.1080/02331930108844556zbMath1049.90093OpenAlexW2004641621MaRDI QIDQ2767580

De-Tong Zhu

Publication date: 2001

Published in: Optimization (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/02331930108844556

zbMATH Keywords

constrained optimizationtrust regionsecant methodsnonmonotonic techniqueFletcher's penalty function

Mathematics Subject Classification ID

Nonlinear programming (90C30) Numerical optimization and variational techniques (65K10)

Cites Work

• Projected quasi-Newton algorithm with trust region for constrained optimization
• Approximate solution of the trust region problem by minimization over two-dimensional subspaces
• A trust region algorithm for equality constrained optimization
• Nonmonotonic trust region algorithm
• Local Convergence of Secant Methods for Nonlinear Constrained Optimization
• On the Local Convergence of a Quasi-Newton Method for the Nonlinear Programming Problem
• A Trust Region Algorithm for Equality Constrained Minimization: Convergence Properties and Implementation
• Projected Hessian Updating Algorithms for Nonlinearly Constrained Optimization
• Continuity of the null space basis and constrained optimization
• A Convergence Theory for a Class of Quasi-Newton Methods for Constrained Optimization
• Global Convergence of a Class of Trust Region Algorithms for Optimization with Simple Bounds
• Newton’s Method with a Model Trust Region Modification
• On the Local Convergence of Quasi-Newton Methods for Constrained Optimization
• A Nonmonotone Line Search Technique for Newton’s Method

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