A differentiable exact penalty function for bound constrained quadratic programming problems

DOI10.1080/02331939108843700zbMath0734.90063OpenAlexW1974221673MaRDI QIDQ3362090

Luigi Grippo, Stefano Lucidi

Publication date: 1991

Published in: Optimization (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/02331939108843700

zbMATH Keywords

approximationscontinuously differentiable exact penalty functionNewton-type algorithmsbound constrained quadratic programming

Mathematics Subject Classification ID

Quadratic programming (90C20) Computational methods for problems pertaining to operations research and mathematical programming (90-08)

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A two-stage active-set algorithm for bound-constrained optimization, Linearly convergent descent methods for the unconstrained minimization of convex quadratic splines, A Newton-type method for positive-semidefinite linear complementarity problems, An exact penalty-lagrangian approach for a class of constrained optimization problems with bounded variables, Canonical dual least square method for solving general nonlinear systems of quadratic equations, Solutions to quadratic minimization problems with box and integer constraints

Cites Work

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• A generalized conjugate gradient algorithm for solving a class of quadratic programming problems
• Iterative Methods for Large Convex Quadratic Programs: A Survey
• An exact penalty function method with global convergence properties for nonlinear programming problems
• Global Convergence of a Class of Trust Region Algorithms for Optimization with Simple Bounds
• Projected Newton Methods for Optimization Problems with Simple Constraints