Multilevel least-change Newton-like methods for equality constrained optimization problems

DOI10.1007/BF02591686zbMath0625.90076OpenAlexW2028913091MaRDI QIDQ3028736

S. M. Grzegórski

Publication date: 1987

Published in: Mathematical Programming (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/bf02591686

zbMATH Keywords

inverse Hessianq-superlinear convergenceequality constrained smooth optimizationmultilevel least-change updatesprojections of fragments of the augmented Hessian matrix

Mathematics Subject Classification ID

Numerical mathematical programming methods (65K05) Nonlinear programming (90C30) Numerical methods based on nonlinear programming (49M37)

Cites Work

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• Diagonalized multiplier methods and quasi-Newton methods for constrained optimization
• Multiplier and gradient methods
• Convergence Theorems for Least-Change Secant Update Methods
• Local and superlinear convergence for truncated iterated projections methods
• Orthogonal Projections on Convex Sets for Newton-Like Methods
• On the Local Convergence of Quasi-Newton Methods for Constrained Optimization
• An Ideal Penalty Function for Constrained Optimization
• Dual Variable Metric Algorithms for Constrained Optimization
• Superlinearly convergent variable metric algorithms for general nonlinear programming problems
• On Sparse and Symmetric Matrix Updating Subject to a Linear Equation
• On the Local and Superlinear Convergence of Quasi-Newton Methods
• A Characterization of Superlinear Convergence and Its Application to Quasi-Newton Methods
• Variations on Variable-Metric Methods

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