首页> 外文期刊>Journal of applied mathematics & decision sciences >On Gradient Simplex Methods for Linear Programs
【24h】

On Gradient Simplex Methods for Linear Programs

机译:线性程序的梯度单纯形法

获取原文
获取原文并翻译 | 示例
           

摘要

A variety of pivot column selection rules based upon the gradient criteria (including the steepest edge) have been explored to improve the efficiency of the primal simplex method. Simplex-like algorithms have been proposed imbedding the gradient direction (GD) which includes all variables whose increase or decrease leads to an improvement in the objective function. Recently a frame work has been developed in the simplex method to incorporate the reduced-gradient direction (RGD) consisting of only variables whose increase leads to an improvement in the objective function. In this paper, the results are extended to embed GD in the simplex method based on the concept of combining directions. Also mathematical properties related to combining directions as well as deleting a variable from all basic directions are presented.
机译:已经探索了各种基于梯度准则(包括最陡峭边缘)的枢轴列选择规则,以提高原始单纯形法的效率。已经提出了类似单纯形的算法,该算法包含了梯度方向(GD),该方向包括所有变量,其增加或减少都会导致目标函数的改善。最近,已经在单纯形方法中开发了一种框架,以合并仅由变量组成的梯度递减方向(RGD),其增加会导致目标函数的改善。本文将结果扩展到基于组合方向概念的单纯形方法中嵌入GD。还介绍了与组合方向以及从所有基本方向删除变量有关的数学属性。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号