A Class of Accelerated Conjugate Direction Methods for Linearly Constrained Minimization Problems

DOI10.2307/2005320zbMath0352.65036OpenAlexW4256452934MaRDI QIDQ4122708

Michael J. Best, Klaus Ritter

Publication date: 1976

Full work available at URL: https://doi.org/10.2307/2005320

Mathematics Subject Classification ID

Numerical mathematical programming methods (65K05) Convex programming (90C25) Nonlinear programming (90C30)

Related Items (6)

A globally and quadratically convergent algorithm for general nonlinear programming problems ⋮ An algorithm for portfolio optimization with variable transaction costs. I: Theory ⋮ Equivalence of some quadratic programming algorithms ⋮ Nonlinear leastpth optimization and nonlinear programming ⋮ A quasi-Newton method can be obtained from a method of conjugate directions ⋮ A method to increase the computational efficiency of certain quadratic programming algorithms

Cites Work

• Accelerating procedures for methods of conjugate directions
• An accelerated conjugate direction method to solve linearly constrained minimization problems
• Minimization of functions having Lipschitz continuous first partial derivatives
• A Method of Conjugate Directions for Linearly Constrained Nonlinear Programming Problems
• A method to accelerate the rate of convergence of a class of optimization algorithms
• A superlinearly convergent method for minimization problems with linear inequality constraints
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