Query minimization under stochastic uncertainty

S. Chaplick, M.M. Halldorsson, M.S. de Lima, T. Tonoyan

Research output: Contribution to journalArticleAcademicpeer-review

Abstract

We study problems with stochastic uncertainty information on intervals for which the precise value can be queried by paying a cost. The goal is to devise an adaptive decision tree to find a correct solution to the problem in consideration while minimizing the expected total query cost. We show that, for the sorting problem, such a decision tree can be found in polynomial time. For the problem of finding the data item with minimum value, we have some evidence for hardness. This contradicts intuition, since the minimum problem is easier both in the online setting with adversarial inputs and in the offline verification setting. However, the stochastic assumption can be leveraged to beat both deterministic and randomized approximation lower bounds for the online setting. (C) 2021 Elsevier B.V. All rights reserved.
Original languageEnglish
Pages (from-to)75-95
Number of pages21
JournalTheoretical Computer Science
Volume895
DOIs
Publication statusPublished - 4 Dec 2021

Keywords

  • Stochastic optimization
  • Query minimization
  • Sorting
  • Selection
  • Online algorithms
  • SPANNING TREE PROBLEM
  • ROBUST
  • OPTIMIZATION
  • COMPLEXITY

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