首页> 外文期刊>Computational optimization and applications >A conjugate direction based simplicial decomposition framework for solving a specific class of dense convex quadratic programs
【24h】

A conjugate direction based simplicial decomposition framework for solving a specific class of dense convex quadratic programs

机译:基于共轭方向的简体分解框架,用于解决特定类密集凸二次程序

获取原文
获取原文并翻译 | 示例
           

摘要

Many real-world applications can usually be modeled as convex quadratic problems. In the present paper, we want to tackle a specific class of quadratic programs having a dense Hessian matrix and a structured feasible set. We hence carefully analyze a simplicial decomposition like algorithmic framework that handles those problems in an effective way. We introduce a new master solver, called Adaptive Conjugate Direction Method, and embed it in our framework. We also analyze the interaction of some techniques for speeding up the solution of the pricing problem. We report extensive numerical experiments based on a benchmark of almost 1400 instances from specific and generic quadratic problems. We show the efficiency and robustness of the method when compared to a commercial solver (Cplex).
机译:许多现实世界应用程序通常可以被建模为凸二次问题。 在本文中,我们希望解决具有密集Hessian矩阵和结构化可行集的特定类别的二次程序。 因此,我们仔细分析了像算法框架的单纯分解,以有效的方式处理这些问题。 我们介绍了一个新的主求解器,称为自适应共轭方向方法,并将其嵌入我们的框架中。 我们还分析了一些技术的相互作用来加速定价问题的解决方案。 我们根据特定和通用二次问题的近1400个实例的基准报告了广泛的数值实验。 与商业求解器(CPLEX)相比,我们展示了该方法的效率和稳健性。

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