MAXIMUM SUBARRAY PROBLEM OPTIMIZATION FOR SPECIFIC DATA

T. Rojek
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Abstract

The maximum subarray problem (MSP) is to the find maximum contiguous sum in an array. This paper describes a method of Kadanes algorithm (the state of the art) optimization for specific data (continuous sequences of zeros or negative real numbers). When the data are unfavourable, the modification of the algorithm causes a non significant performance loss (1% > decrease in performance). The modification does not improve time complexity but reduces the number of elementary operations. Various experimental data sets have been used to evaluate possible time efficiency improvement. For the most favourable data sets an increase in efficiency of 25% can be achieved.
针对特定数据的最大子阵列问题优化
最大子数组问题(MSP)是在一个数组中找到最大连续和的问题。本文描述了一种针对特定数据(连续的零序列或负实数)的Kadanes算法(目前最先进的)优化方法。当数据不理想时,对算法的修改会造成不明显的性能损失(性能下降> 1%)。这种修改没有提高时间复杂度,但减少了基本操作的数量。各种实验数据集被用来评估可能的时间效率改进。对于最有利的数据集,可以实现25%的效率提高。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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