Grossman T, Wool A. Computational experience with approximation algorithms for the set covering problem. European Journal of Operational Research 1997;101.T. Grossman and A. Wool. Computational experience with approximation algorithms for the set covering problem. European Journal of Operational Research,...
Lessing, L., Dumitrescu, I., Sttzle, T.: A comparison between ACO algorithms for the set covering problem. In: LNCS 3172, Springer. pp. 1-12 (2004)Lessing, L., Dumitrescu, I. and Sttzle, T., A comparison between ACO algorithms for the set covering problem. Lecture Notes in ...
Efficient heuristic algorithms for the weighted set covering problem. Computers and Oper. Research, 8(4):303-310, 1981.Efficient Heuristic Algorithm For the Weighted Set Covering Problem - K - 1981Efficient heuristic algorithms for the weighted set covering problem - Baker - 1981...
Proof of the master theorem(主定理的证明)(118) 1. The proof for exact powers(确切权力的证明)(118) 2. Floors and ceilings(地板和天花板)(123) 5. Probabilistic Analysis and Randomized Algorithms(概率分析和随机算法)(134) 1. The hiring problem(招聘问题)(134) 1. Worst-case analysis(最坏情况...
Fast parallel molecular solution to the dominating-set problem on massively parallel bio-computing This paper shows how to use DNA strands to construct solution space of molecules for the dominating-set problem and how to apply biological operations to s... Minyi Guo,Michael Shan-Hui Ho,Weng-Lon...
We study the classic NP-Hard problem of finding the maximum k-set coverage in the data stream model: given a set system of m sets that are subsets of a universe {1,…,n}, find the k sets that cover the most number of distinct elements. The problem can be approximated up to a fact...
3.Weproposethefirstnon-trivialapproximationforthek-generalized connectivity problem. 4. We improve on the currently best approximation for directed Steiner network. 5. We present a unified framework for approximating partial covering problems, and demon- strate the applicability of our method in ...
34.5 NP-complete problems Problems 35 Approximation Algorithms 35.1 The vertex-cover problem 35.2 The traveling-salesman problems 35.3 The set-covering problem 35.4 Randomization and linear programming 35.5 The subset-sum problem ProblemAboutSome exercises and problems in Introduction to Algorithms 3rd editi...
The present study examines the role of feature selection methods in optimizing machine learning algorithms for predicting heart disease. The Cleveland Heart disease dataset with sixteen feature selection techniques in three categories of filter, wrapper,
One way of solving this problem is using a Hashtable or HashMap data structure. You can traverse through array, and store each number as key and number of occurrence as value. At the end of traversal you can find all duplicate numbers, for which occurrence is more than one. In Java if...