Topic
Average-case complexity
About: Average-case complexity is a research topic. Over the lifetime, 1749 publications have been published within this topic receiving 44972 citations.
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28 Apr 2005TL;DR: A modified union- find algorithm that represents the data in an array rather than the commonly used pointer-based data structures is presented, and a simpler proof that the average case complexity of the union-find algorithm is linear is presented.
Abstract: We present a modified union-find algorithm that represent the data in an array rather than the commonly used pointer-based data structures, and a simpler proof that the average case complexity of the union-find algorithm is linear
2 citations
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TL;DR: In this paper, it was shown that for fixed dimensionn, the approximation of inner and outer j-radii of polytopes in ℝn, endowed with the Euclidean norm, is in √ n.
Abstract: We show that, for fixed dimensionn, the approximation of inner and outer j-radii of polytopes in ℝn, endowed with the Euclidean norm, is in ℙ. Our method is based on the standard polynomial time algorithms for solving a system of polynomial inequalities over the reals in fixed dimension.
2 citations
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22 Aug 1994TL;DR: A function, B(x) is introduced which assigns a real number to a string, x, which is intended to be a measure of the randomness of x, the Kolmogorov complexity of x.
Abstract: A function, B(x) is introduced which assigns a real number to a string, x, which is intended to be a measure of the randomness of x. Comparisons are made between B(x) and K(x), the Kolmogorov complexity of x. A O(n3) algorithm for computing B(x) is given, along with brief descriptions of experimental results showing the efficacy of this function in practical situations.
2 citations
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TL;DR: In this paper, Stanowski et al. define complexity definition MARIUSZ STANOWSKI Independent (Poland) and present it in terms of complexity.
Abstract: Complexity Definition MARIUSZ STANOWSKI Independent (Poland)
2 citations
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07 Jul 2012TL;DR: The structural characteristics that can help to predict the complexity of NK-landscape instances for estimation of distribution algorithms (EDAs) are investigated and network measures are identified that have a statistically significant difference between the set of easy and hard instances.
Abstract: In this paper we empirically investigate the structural characteristics that can help to predict the complexity of NK-landscape instances for estimation of distribution algorithms (EDAs). We evolve instances that maximize the EDA complexity in terms of its success rate. Similarly, instances that minimize the algorithm complexity are evolved. We then identify network measures, computed from the structures of the NK-landscape instances, that have a statistically significant difference between the set of easy and hard instances. The features identified are consistently significant for different values of $N$ and $K$.
2 citations