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Exploiting Structures in Mixed-Integer Second-Order Cone Optimization Problems for Branch-and-Conic-Cut Algorithms

机译:分支整数法的混合整数二阶锥优化问题中的开发结构

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摘要

This thesis studies computational approaches for mixed-integer second-order cone optimization (MISOCO) problems. MISOCO models appear in many real-world applications, so MISOCO has gained significant interest in recent years. However, despite recent advancements, there is a gap between the theoretical developments and computational practice. Three chapters of this thesis address three areas of computational methodology for an efficient branch-and-conic-cut (BCC) algorithm to solve MISOCO problems faster in practice. These chapters include a detailed discussion on practical work on adding cuts in a BCC algorithm, novel methodologies for warm-starting second-order cone optimization (SOCO) subproblems, and heuristics for MISOCO problems.;The first part of this thesis concerns the development of a novel warm-starting method of interior-point methods (IPM) for SOCO problems. The method exploits the Jordan frames of an original instance and solves two auxiliary linear optimization problems. The solutions obtained from these problems are used to identify an ideal initial point of the IPM. Numerical results on public test sets indicate that the warm-start method works well in practice and reduces the number of iterations required to solve related SOCO problems by around 30-40%.;The second part of this thesis presents novel heuristics for MISOCO problems. These heuristics use the Jordan frames from both continuous relaxations and penalty problems and present a way of finding feasible solutions for MISOCO problems. Numerical results on conic and quadratic test sets show significant performance in terms of finding a solution that has a small gap to optimality.;The last part of this thesis presents application of disjunctive conic cuts (DCC) and disjunctive cylindrical cuts (DCyC) to asset allocation problems (AAP). To maximize the benefit from these powerful cuts, several decisions regarding the addition of these cuts are inspected in a practical setting. The analysis in this chapter gives insight about how these cuts can be added in case-specific settings.
机译:本文研究了混合整数二阶锥优化(MISOCO)问题的计算方法。 MISOCO模型出现在许多实际应用中,因此近年来MISOCO引起了极大的兴趣。但是,尽管有最近的进步,但是理论发展和计算实践之间仍然存在差距。本文的三章论述了一种有效的分支和圆锥形切分(BCC)算法的计算方法的三个领域,该算法可在实践中更快地解决MISOCO问题。这些章节包括有关在BCC算法中增加切入量的实际工作的详细讨论,热启动二阶锥优化(SOCO)子问题的新颖方法以及MISOCO问题的启发式方法。解决SOCO问题的新颖的内点法(IPM)的热启动方法。该方法利用原始实例的Jordan框架并解决了两个辅助线性优化问题。从这些问题中获得的解决方案用于确定IPM的理想起点。公开测试集上的数值结果表明,热启动方法在实践中效果很好,将解决相关SOCO问题所需的迭代次数减少了约30-40%。;本文的第二部分提出了MISOCO问题的新颖启发式方法。这些启发式方法使用来自连续松弛和罚分问题的Jordan框架,并提出了一种找到MISOCO问题的可行解决方案的方法。圆锥和二次试验集上的数值结果表明,在寻找与最优性差距不大的解决方案方面具有显着的性能。本论文的最后一部分介绍了离散圆锥形切割(DCC)和圆柱形圆柱状切割(DCyC)在资产上的应用分配问题(AAP)。为了从这些功能强大的切割中获得最大收益,在实际环境中会检查有关添加这些切割的若干决定。本章中的分析提供了有关如何在特定案例的设置中添加这些削减的见解。

著录项

  • 作者

    Cay, Sertalp B.;

  • 作者单位

    Lehigh University.;

  • 授予单位 Lehigh University.;
  • 学科 Industrial engineering.
  • 学位 Ph.D.
  • 年度 2018
  • 页码 196 p.
  • 总页数 196
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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