A Novel Neural Network Model for Solving Programming Problems Based on Linear Restraints;

Firstly, for the stochastic programming problem, we propose the concept of synthesizing effect function, and give the stochastic programming model based on the synthesizing effect function by processing objective function and constraints, and analyze the reliability; secondly, for the multi-objective stochastic programming problem, we use the concept

In this paper, we make further study on the sub-programming of the simplex-like method with bisection, provide a new special linear sub-programming to improve the infeasibility of the problem, determine the corresponding pivoting rule,and set up a new algorithm according to the new sub-programming.

The first two cases can be converted into quadratic model or convex programming problem with quadratic constraint, but the third one is a nonconvex programming problem.

A new method for seeking initial basic feasible solution of linear programming problem;

A primal greedy algorithm for solving a class of linear programming problems and a dual greedy algorithm for solving its dual problems are presented.

This paper gives the necessary and sufficient condition for infinitude best solution concerning linear programming problem to illustrate that when in such case,we can at least draw a conclusion that there exists one non vibrating test number.