Random number generators: good ones are hard to find
Communications of the ACM
Solving airline crew scheduling problems by branch-and-cut
Management Science
On maximum clique problems in very large graphs
External memory algorithms
Finding maximum independent sets in graphs arising from coding theory
Proceedings of the 2002 ACM symposium on Applied computing
Computers and Intractability: A Guide to the Theory of NP-Completeness
Computers and Intractability: A Guide to the Theory of NP-Completeness
Modeling and Solving the Crew Rostering Problem
Operations Research
Optimizing synchronization in multiprocessor DSP systems
IEEE Transactions on Signal Processing
A Stochastic Local Search Approach to Vertex Cover
KI '07 Proceedings of the 30th annual German conference on Advances in Artificial Intelligence
A mixed integer linear program and tabu search approach for the complementary edge covering problem
Advances in Engineering Software
Set cover algorithms for very large datasets
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Survey: Covering problems in facility location: A review
Computers and Industrial Engineering
A hybridized tabu search approach for the minimum weight vertex cover problem
Journal of Heuristics
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Several approximation algorithms with proven performance guarantees have been proposed to find approximate solutions to classical combinatorial optimization problems. However, theoretical results may not reflect the experimental performance of the proposed algorithms. As a consequence, a question arises: how "far" from the theoretically proved performance are the experimental results? We conduct a controlled empirical study of approximation algorithms for the Vertex Cover and the Set Covering Problems. Many authors have proposed approximation algorithms for those problems. Our main goal is to better understand their strengths, weaknesses, and operation. Although we implement more than one algorithm to find feasible solutions to either problems, this work does not emphasize competition between them. The quality of the solutions related to the theoretical performance guarantees are analyzed instead. The computational experiments showed that the proven performance guarantees of all tested algorithms did not forecast well the empirical performance.