A Global Convergence Theory for Dennis, El-Alem, and Maciel's Class of Trust-Region Algorithms for Constrained Optimization without Assuming Regularity

DOI10.1137/S1052623497331762zbMath0957.65059OpenAlexW1993406823MaRDI QIDQ4702318

Mahmoud M. El-Alem

Publication date: 24 November 1999

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

Full work available at URL: https://doi.org/10.1137/s1052623497331762

zbMATH Keywords

global convergenceconstrained optimizationnonlinear programmingtrust region methodstationary pointsaugmented Lagrangianregularity assumptionequality constrained problemsMayer-Bliss pointsquasi-normal step

Mathematics Subject Classification ID

Numerical mathematical programming methods (65K05) Nonlinear programming (90C30)

Related Items (11)

Nonlinear programming without a penalty function or a filter ⋮ Nonmonotone trust-region method for nonlinear programming with general constraints and simple bounds ⋮ An interior-point penalty active-set trust-region algorithm ⋮ A global convergence theory for an active-trust-region algorithm for solving the general nonlinear programing problem. ⋮ A Trust Region Algorithm with Memory for Equality Constrained Optimization ⋮ Global convergence of a nonmonotone filter method for equality constrained optimization ⋮ A multiplier active-set trust-region algorithm for solving constrained optimization problem ⋮ An adaptive augmented Lagrangian method for large-scale constrained optimization ⋮ A penalty-free-type nonmonotone trust-region method for nonlinear constrained optimization ⋮ A robust trust region method for nonlinear optimization with inequality constraint ⋮ Ghost Penalties in Nonconvex Constrained Optimization: Diminishing Stepsizes and Iteration Complexity

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