AbstractWe offer a variant of the piecewise-linear penalty-function approach to linear programming which was proposed by Conn [5]. Our variant makes use of computational techniques which are more closely related to those in existing computer codes for linear programming and which can be more readily adapted for large sparse problems that were the techniques described by Conn. An experimental code for small dense problems has been prepared and some experience with it is reported
The computational aspects of the simplex algorithm are investigated, and high performance computing ...
Linear Programming (LP) is a powerful decision making tool extensively used in various economic and ...
We propose and analyze two classes of neural network models for solving linear programming (LP) prob...
This paper presents a variant of logarithmic penalty methods for nonlinear convex programming. If th...
We give a general description of a new advanced implementation of the simplex method for linear prog...
We use quadratic penalty functions along with some recent ideas from linear l1 estimation to arrive ...
The study deals with nonlinear programming. The work is aimed at development of rather simple, as re...
We are interested in a class of linear bilevel programs where the upper level is a linear scalar opt...
Potential Function Methods For Approximately Solving Linear Programming Problems breaks new ground i...
In this paper we present a reduced-gradient type algorithm for solving large-scale linearly constrai...
Abstract. We consider the following classes of nonlinear programming problems: the minimization of s...
The non-linear programming problem seeks to maximize a function f(x) where the n component vector x ...
This paper presents an algorithm model for the analysis of penalty functions type algorithms. Two op...
This paper takes a fresh look at the application of quadratic penalty functions to linear programmin...
A fast Newton method is proposed for solving linear programs with a very large (# 10 ) number of...
The computational aspects of the simplex algorithm are investigated, and high performance computing ...
Linear Programming (LP) is a powerful decision making tool extensively used in various economic and ...
We propose and analyze two classes of neural network models for solving linear programming (LP) prob...
This paper presents a variant of logarithmic penalty methods for nonlinear convex programming. If th...
We give a general description of a new advanced implementation of the simplex method for linear prog...
We use quadratic penalty functions along with some recent ideas from linear l1 estimation to arrive ...
The study deals with nonlinear programming. The work is aimed at development of rather simple, as re...
We are interested in a class of linear bilevel programs where the upper level is a linear scalar opt...
Potential Function Methods For Approximately Solving Linear Programming Problems breaks new ground i...
In this paper we present a reduced-gradient type algorithm for solving large-scale linearly constrai...
Abstract. We consider the following classes of nonlinear programming problems: the minimization of s...
The non-linear programming problem seeks to maximize a function f(x) where the n component vector x ...
This paper presents an algorithm model for the analysis of penalty functions type algorithms. Two op...
This paper takes a fresh look at the application of quadratic penalty functions to linear programmin...
A fast Newton method is proposed for solving linear programs with a very large (# 10 ) number of...
The computational aspects of the simplex algorithm are investigated, and high performance computing ...
Linear Programming (LP) is a powerful decision making tool extensively used in various economic and ...
We propose and analyze two classes of neural network models for solving linear programming (LP) prob...