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Combinatorial Algorithms: Theory and Practice
The subset‐sum problem (SSP) is defined as follows: given a positive integer bound and a set of n positive integers find a subset whose sum is closest to, but not greater than, the bound. We present a randomized approximation algorithm for this problem with linear space complexity and time complexity of O(n log n). Experiments with random uniformly‐distributed instances of SSP show that our algorithm outperforms, both in running time and average error, Martello and Toth's (1984) quadratic greedy search, whose time complexity is O(n2). We propose conjectures on the expected error of our algorithm for uniformly‐distributed instances of SSP and provide some analytical arguments justifying these conjectures. We present also results of numerous tests.
International Transactions in Operational Research – Wiley
Published: Jul 1, 2002
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