Using the KKT matrix in an augmented Lagrangian SQP method for sparse constrained optimization

DOI10.1007/BF02192305zbMath0823.90110MaRDI QIDQ1893348

M. de F. G. Hernandez, Michael Bartholomew-Biggs

Publication date: 1995

Published in: Journal of Optimization Theory and Applications (Search for Journal in Brave)

zbMATH Keywords

constrained optimizationsequential quadratic programmingsparse data structures

Mathematics Subject Classification ID

Numerical mathematical programming methods (65K05) Nonlinear programming (90C30) Quadratic programming (90C20) Computational methods for problems pertaining to operations research and mathematical programming (90-08)

Related Items (3)

A trust-region algorithm for equality-constrained optimization via a reduced dimension approach. ⋮ A regularization method for constrained nonlinear least squares ⋮ New theoretical results on recursive quadratic programming algorithms

Uses Software

• LANCELOT
• OPALQP
• MA27

Cites Work

• Modifications to the subroutine OPALQP for dealing with large problems
• Local convergence analysis for the REQP algorithm using conjugate basis matrices
• On the Accurate Determination of Search Directions for Simple Differentiable Penalty Functions
• A New Modified Cholesky Factorization
• Recursive quadratic programming methods based on the augmented lagrangian
• A note about sparsity exploiting quasi-Newton updates
• A projected Lagrangian algorithm and its implementation for sparse nonlinear constraints
• Solving Large Sparse Nonlinear Programs Using GRG
• On Sparse and Symmetric Matrix Updating Subject to a Linear Equation
• The Factorization of Sparse Symmetric Indefinite Matrices
• An Optimal Positive Definite Update for Sparse Hessian Matrices
• Newton Methods for Large-Scale Linear Equality-Constrained Minimization

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