A projected conjugate gradient method for sparse minimax problems

DOI10.1007/BF02108465zbMath0789.65047MaRDI QIDQ1315210

Kristján Jónasson

Publication date: 24 February 1994

Published in: Numerical Algorithms (Search for Journal in Brave)

zbMATH Keywords

numerical resultsconjugate gradientstrust region methodssequential linear programmingnonlinear minimax problemsfirst order method

Mathematics Subject Classification ID

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

Related Items (2)

An algebraic approach to lens distortion by line rectification ⋮ Approximation in normed linear spaces

Uses Software

• LANCELOT
• MMLA1Q

Cites Work

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• A smooth method for the finite minimax problem
• Discrete, nonlinear approximation problems in polyhedral norms: A Levenberg-like algorithm
• Strong uniqueness: A far-reaching criterion for the convergence analysis of iterative procedures
• Corrected sequential linear programming for sparse minimax optimization
• Nonlinear programming and nonsmooth optimization by successive linear programming
• Variable metric methods for minimizing a class of nondifferentiable functions
• Combined lp and quasi-Newton methods for minimax optimization
• A Projected Lagrangian Algorithm for Nonlinear Minimax Optimization
• A model algorithm for composite nondifferentiable optimization problems
• An Algorithm for Minimax Solution of Overdetermined Systems of Non-linear Equations
• An Efficient Method to Solve the Minimax Problem Directly
• The Minimax Solution of an Overdetermined System of Non-linear Equations
• An Algorithm for Minimax Approximation in the Nonlinear Case

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