Convergence Properties of Minimization Algorithms for Convex Constraints Using a Structured Trust Region

DOI10.1137/S1052623492236481zbMath0868.90106MaRDI QIDQ4717557

Annick Sartenaer, Andrew R. Conn, Phillipe L. Toint, Nick I. M. Gould

Publication date: 1 December 1996

Published in: SIAM Journal on Optimization (Search for Journal in Brave)

zbMATH Keywords

global convergencepartial separabilitytrust region algorithmsconvex feasible regionsstructured problemslarge-scale nonlinear applications

Mathematics Subject Classification ID

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

Related Items

A matrix-free trust-region Newton algorithm for convex-constrained optimization, Recent progress in unconstrained nonlinear optimization without derivatives, Numerical experiments with the Lancelot package (Release \(A\)) for large-scale nonlinear optimization, Global convergence of trust-region algorithms for convex constrained minimization without derivatives, Local search proximal algorithms as decision dynamics with costs to move, An alternating structured trust region algorithm for separable optimization problems with nonconvex constraints, Complexity of Partially Separable Convexly Constrained Optimization with Non-Lipschitzian Singularities, Optimizing partially separable functions without derivatives, High-order evaluation complexity for convexly-constrained optimization with non-Lipschitzian group sparsity terms, A trust-region framework for real-time optimization with structural process-model mismatch

Uses Software

• LANCELOT
• ve08