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arXiv 1902.02587math.OC
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本地快速学习的学习计划

Local Rapid Learning for Integer Programs

Timo Berthold, Peter J. Stuckey, Jakob Witzig

摘要:

冲突学习算法是现代MIP和CP求解器的重要组成部分。但是,强冲突信息通常是通过深度优先搜索获得的。虽然这是CP求解的自然模式,但它不适用于MIP求解。快速学习是一种混合CP/MIP方法,其中CP搜索应用于根以学习信息来支持剩余的MIP求解。这已被证明是有益的二进制程序。在本文中,我们将快速学习的思想扩展到整数规划,其中并非所有变量都限制在域{0,1}中,而不仅仅是在根处运行快速CP搜索,我们将在MIP搜索树内的局部搜索节点处重复应用它。为了有效地做到这一点,我们提出了六个启发式标准来预测本地\rapidlearning成功的机会。我们的计算实验表明,我们的扩展快速学习算法显着加快MIP搜索,是特别有益的高度对偶退化问题。

英文摘要:

Conflict learning algorithms are an important component of modern MIP and CP solvers. But strong conflict information is typically gained by depth-first search. While this is the natural mode for CP solving, it is not for MIP solving. Rapid Learning is a hybrid CP/MIP approach where CP search is applied at the root to learn information to support the remaining MIP solve. This has been demonstrated to be beneficial for binary programs. In this paper, we extend the idea of Rapid Learning to integer programs, where not all variables are restricted to the domain {0,1}, and rather than just running a rapid CP search at the root, we will apply it repeatedly at local search nodes within the MIP search tree. To do so efficiently, we present six heuristic criteria to predict the chance for local \rapidlearning to be successful. Our computational experiments indicate that our extended Rapid Learning algorithm significantly speeds up MIP search and is particularly beneficial on highly dual degenerate problems.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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