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In combinatorial optimization, a popular approach to NP-hard problems is the design of approximation algorithms. These algorithms typically run in polynomial time and are guaranteed to produce a solution which is within a known multiplicative factor of optimal. Unfortunately, the known factor is often known to be large in pathological instances. Conventional wisdom holds that, in practice, approximation algorithms will produce solutions closer to optimal than their proven guarantees. In thisdoi:10.1145/1060590.1060656 dblp:conf/stoc/FlaxmanFV05 fatcat:lmgeeypfh5cvxao4zz4zhvmt54