A computational study of active set strategies in nonlinear programming with linear constraints

DOI10.1007/BF01582095zbMath0402.90084OpenAlexW2067791548MaRDI QIDQ4187599

M. L. Lenard

Publication date: 1979

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

Full work available at URL: https://doi.org/10.1007/bf01582095

zbMATH Keywords

Nonlinear ProgrammingLinear ConstraintsComputational StudyActive Set Strategies

Mathematics Subject Classification ID

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

Related Items (6)

A two-stage successive overrelaxation algorithm for solving the symmetric linear complementarity problem ⋮ An active set method for solving linearly constrained nonsmooth optimization problems ⋮ An algorithm for linearly constrained programs with a partly linear objective function ⋮ An active-constraint logic for non-linear programming ⋮ An active set strategy for solving optimization problems with up to 200,000,000 nonlinear constraints ⋮ Active set algorithms for isotonic regression; a unifying framework

Cites Work

• Unnamed Item
• The Gradient Projection Method for Nonlinear Programming. Part I. Linear Constraints
• Newton-type methods for unconstrained and linearly constrained optimization
• Projection methods for non-linear programming
• Matrix factorizations in optimization of nonlinear functions subject to linear constraints
• A Rapidly Convergent Descent Method for Minimization
• Extension of Davidon’s Variable Metric Method to Maximization Under Linear Inequality and Equality Constraints
• Computational experience with quadratically convergent minimisation methods
• A General Quadratic Programming Algorithm

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