A globally convergent trust region algorithm for optimization with general constraints and simple bounds.

DOI10.1007/BF02684044zbMath1052.90611OpenAlexW2050613758MaRDI QIDQ1864225

Zhongwen Chen, Qiaoming Han, Ji-ye Han

Publication date: 17 March 2003

Published in: Acta Mathematicae Applicatae Sinica. English Series (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/bf02684044

Mathematics Subject Classification ID

Nonlinear programming (90C30) Methods of quasi-Newton type (90C53)

Related Items (4)

A globally convergent penalty-free method for optimization with equality constraints and simple bounds ⋮ A nonmonotone trust-region algorithm with nonmonotone penalty parameters for constrained optimization ⋮ Nonmonotone trust-region method for nonlinear programming with general constraints and simple bounds ⋮ A penalty-free-type nonmonotone trust-region method for nonlinear constrained optimization

Cites Work

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• The nonlinear programming method of Wilson, Han, and Powell with an augmented Lagrangian type line search function. I. Convergence analysis
• A trust region algorithm for equality constrained optimization
• A trust-region strategy for minimization on arbitrary domains
• A Trust Region Algorithm for Nonlinearly Constrained Optimization
• A necessary and sufficient regularity condition to have bounded multipliers in nonconvex programming
• On Perturbations in Systems of Linear Inequalities

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