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In Defense of Simplex’ Worst-Case Behavior

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In the early 1970s, by work of Klee and Minty (1972) and Zadeh (1973), the Simplex Method, the Network Simplex Method, and the Successive Shortest Path Algorithm have been proved guilty of exponential worst-case behavior (for certain pivot rules). Since then, the common perception is that these algorithms can be fooled into investing senseless effort by ‘bad instances’ such as, e.g., Klee-Minty cubes. This talk promotes a more favorable stance towards the algorithms’ worst-case behavior. We argue that the exponential worst-case performance is not necessarily a senseless waste of time, but may rather be due to the algorithms performing meaningful operations and solving difficult problems on their way. Given one of the above algorithms as a black box, we show that using this black box, with polynomial overhead and a limited interface, we can solve any problem in NP. This also allows us to derive NP-hardness results for some related problems.

This talk is part of the Optimization and Incentives Seminar series.

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