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Prepare for the Expected Worst: Algorithms for Reconfigurable Resources Under Uncertainty

Authors David Ellis Hershkowitz, R. Ravi, Sahil Singla

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Author Details

David Ellis Hershkowitz
  • Carnegie Mellon University, Pittsburgh, PA, USA
R. Ravi
  • Carnegie Mellon University, Pittsburgh, PA, USA
Sahil Singla
  • Princeton University, Princeton, NJ, USA
  • Institute for Advanced Study, Princeton, NJ, USA

Funding

  • Hershkowitz, David Ellis: Supported in part by NSF grants CCF-1527110, CCF-1618280, CCF-1814603, CCF-1910588, NSF CAREER award CCF-1750808 and a Sloan Research Fellowship.
  • Ravi, R.: Supported in part by the U. S. Office of Naval Research award N00014-18-1-2099, and the U. S. NSF award CCF-1527032.
  • Singla, Sahil: Supported in part by Schmidt Foundation and NSF awards CCF-1319811, CCF-1536002, and CCF-1617790.

Cite AsGet BibTex

David Ellis Hershkowitz, R. Ravi, and Sahil Singla. Prepare for the Expected Worst: Algorithms for Reconfigurable Resources Under Uncertainty. In Approximation, Randomization, and Combinatorial Optimization. Algorithms and Techniques (APPROX/RANDOM 2019). Leibniz International Proceedings in Informatics (LIPIcs), Volume 145, pp. 4:1-4:19, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2019)
https://doi.org/10.4230/LIPIcs.APPROX-RANDOM.2019.4

Abstract

In this paper we study how to optimally balance cheap inflexible resources with more expensive, reconfigurable resources despite uncertainty in the input problem. Specifically, we introduce the MinEMax model to study "build versus rent" problems. In our model different scenarios appear independently. Before knowing which scenarios appear, we may build rigid resources that cannot be changed for different scenarios. Once we know which scenarios appear, we are allowed to rent reconfigurable but expensive resources to use across scenarios. Although computing the objective in our model might seem to require enumerating exponentially-many possibilities, we show it is well estimated by a surrogate objective which is representable by a polynomial-size LP. In this surrogate objective we pay for each scenario only to the extent that it exceeds a certain threshold. Using this objective we design algorithms that approximately-optimally balance inflexible and reconfigurable resources for several NP-hard covering problems. For example, we study variants of minimum spanning and Steiner trees, minimum cuts, and facility location. Up to constants, our approximation guarantees match those of previously-studied algorithms for demand-robust and stochastic two-stage models. Lastly, we demonstrate that our problem is sufficiently general to smoothly interpolate between previous demand-robust and stochastic two-stage problems.

Subject Classification

ACM Subject Classification
  • Theory of computation → Approximation algorithms analysis
  • Theory of computation → Packing and covering problems
  • Theory of computation → Routing and network design problems
  • Theory of computation → Facility location and clustering
  • Theory of computation → Rounding techniques
Keywords
  • Approximation Algorithms
  • Optimization Under Uncertainty
  • Two-Stage Optimization
  • Expected Max

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