bkz仿真中枚举解的更好采样方法

G. R. Moghissi, A. Payandeh
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引用次数: 1

摘要

实际运行无法研究BKZ算法在较大块大小时的确切方式,因此可以通过BKZ的仿真来预测BKZ算法的总成本和输出质量。枚举解向量v的采样方法是设计bkz仿真的主要组成部分之一,可分为两个阶段:解向量v的采样范数和采样对应系数向量。本文介绍了对任意枚举边界函数的成功概率对枚举解v的范数进行采样的一种简单有效的思路,而据我们所知,以往的研究中没有提出过对枚举解的范数进行采样的方法。其次,本文分析了系数向量的结构和概率分布(对应于枚举解v),进而介绍了这些系数向量的采样方法,并通过我们的测试结果进行了验证,而以往的bkz -simulation设计中并没有对采样系数向量进行深入的分析。此外,本文还提出了用最优边界函数剪枝的枚举代价的近似。
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Better Sampling Method of Enumeration Solution for BKZ-Simulation
The exact manner of BKZ algorithm for higher block sizes cannot be studied by practical running, so simulation of BKZ can be used to predict the total cost and output quality of BKZ algorithm. Sampling method of enumeration solution vector v is one of the main components of designing BKZ-simulation and can be divided into two phases: sampling norm of solution vector v and sampling corresponding coefficient vectors. This paper introduces a simple and efficient idea for sampling the norm of enumeration solution v for any success probability of enumeration bounding functions, while to the best of our knowledge, no such sampling method for norm of enumeration solution is proposed in former studies. Next, this paper analyzes the structure and probability distribution of coefficient vectors (corresponding with enumeration solution v), and consequently introduces the sampling methods for these coefficient vectors which are verified by our test results, while no such a deep analysis for sampling coefficient vectors is considered in design of former BKZ-simulations. Moreover, this paper proposes an approximation for cost of enumerations pruned by optimal bounding functions.
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