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Sub-optimality Approximations

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Part of the Lecture Notes in Computer Science book series (LNPSE,volume 3709)

Abstract

The sub-optimality approximation problem considers an optimization problem \(\mathcal{O}\), its optimal solution σ *, and a variable x with domain {d 1,...,d m } and returns approximations to \(\mathcal{O}\)[\(x \longleftarrow d_1\)],...,\(\mathcal{O}\)[\(x \longleftarrow d_m\)], where \(\mathcal{O}\)[\(x \longleftarrow d_1\)] denotes the problem \(\mathcal{O}\) with x assigned to d i . The sub-optimality approximation problem is at the core of online stochastic optimization algorithms and it can also be used for solution repair and approximate filtering of optimization constraints. This paper formalizes the problem and presents sub-optimality approximation algorithms for metric TSPs, packet scheduling, and metric k-medians that run faster than the optimal or approximation algorithms. It also presents results on the hardness/easiness of sub-optimality approximations.

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  • DOI: 10.1007/11564751_12
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Bent, R., Katriel, I., Van Hentenryck, P. (2005). Sub-optimality Approximations. In: van Beek, P. (eds) Principles and Practice of Constraint Programming - CP 2005. CP 2005. Lecture Notes in Computer Science, vol 3709. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11564751_12

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  • DOI: https://doi.org/10.1007/11564751_12

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-29238-8

  • Online ISBN: 978-3-540-32050-0

  • eBook Packages: Computer ScienceComputer Science (R0)