Numerical experience with conjugate direction methods in constrained minimization

DOI10.1016/0377-2217(89)90322-6zbMath0684.90087OpenAlexW2043639228MaRDI QIDQ1825773

P. J. Reddy, B. S. N. Murty, Asghar Husain

Publication date: 1989

Published in: European Journal of Operational Research (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/0377-2217(89)90322-6

zbMATH Keywords

test problemsconstrained minimizationKuhn-Tucker conditionsconjugate direction methodsaccelerated computer codeleast square algorithmsequential conjugate gradient- restoration algorithm

Mathematics Subject Classification ID

Numerical mathematical programming methods (65K05) Nonlinear programming (90C30) Methods of reduced gradient type (90C52)

Cites Work

• Unnamed Item
• Unnamed Item
• Orthogonality correction in the conjugate-gradient method
• A least-square algorithm for nonlinear optimization problems
• An accelerated multiplier method for nonlinear programming
• A computational method for minimization with nonlinear constraints
• The prerestorative step in the sequential gradient-restoration algorithm for mathematical programming problems with inequality constraints
• CGS algorithms for constrained extremization of functions
• CGS algorithms for unconstrained minimization of functions
• Sequential gradient-restoration algorithm for the minimization of constrained functions. Ordinary and conjugate gradient versions
• Function minimization by conjugate gradients
• A Numerical Method for Finding Solutions of Nonlinear Equations
• A computational comparison of some non-linear programs
• On Isometries of Euclidean Spaces

This page was built for publication: Numerical experience with conjugate direction methods in constrained minimization