This paper presents a infeasible interior-point primal -dual affine scaling algorithm for linear programming. it is shown that the method is polynomial-time algorithm.
摘要本文对线性规划提出了一个不可行内点原始-对偶仿射尺度算法，并证明了算法是一个多项式时间算法。
To solve a linear programming with the dual simplex algorithm, it is necessary to find a primal regular solution.
在用对偶单纯形法解线性规划问题时，必须找到初始正则解。
First, the main procedures and the distinctive features of the most-obtuse-angle (MOA) row or column pivot rules are introduced for achieving primal or dual feasibility in linear programming.
首先回顾了采用最钝角行、列主元规则求解线性规画问题的原始、对偶可行解的主要过程，阐述了其与众不同的特性。
On the Research of Primal-dual Infeasible Interior Point Algorithm for Box Linear Programming;
给出了二次锥规划的一种非精确不可行内点算法。
A primal-dual neural network(PDNN)based on linear variational inequalities(LVIs)is introduced as the real-time solver for the resultant quadratic programming scheme.
提出了基于线性变分不等式的原对偶神经网络，并将其作为所对应的二次型规划方案的实时求解器。
A primal-dual neural network(PDNN)based on linear variational inequalities(LVIs)is introduced as the real-time solver for the resultant quadratic programming scheme.
提出了基于线性变分不等式的原对偶神经网络，并将其作为所对应的二次型规划方案的实时求解器。
应用推荐