Query Minimization Under Stochastic Uncertainty

Steven Chaplick, Magnús M. Halldórsson, Murilo Santos de Lima, Tigran Tonoyan

Research output: Chapter in Book/Report/Conference proceedingChapterAcademic


We study problems with stochastic uncertainty data 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 sorting in this scenario can be performed in polynomial time, while finding the data item with minimum value seems to be hard. 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. Although some literature has been devoted to minimizing query/probing costs when solving uncertainty problems with stochastic input, none of them have considered the setting we describe. Our approach is closer to the study of query-competitive algorithms, and it gives a better perspective on the impact of the stochastic assumption.keywordsstochastic optimizationquery minimizationsortingselectiononline algorithms.
Original languageEnglish
Title of host publicationLatin American Symposium on Theoretical Informatics
Subtitle of host publicationLATIN 2020: Theoretical Informatics
EditorsY. Kohayakawa, F.K. Miyazawa
PublisherSpringer, Cham
ISBN (Electronic)978-3-030-61792-9
ISBN (Print)978-3-030-61791-2
Publication statusPublished - 2020
Event14th Latin American Symposium on Theoretical Informatics: Theoretical Informatics - Sao Paulo, Brazil
Duration: 5 Jan 20218 Jan 2021

Publication series

SeriesLecture Notes in Computer Science


Symposium14th Latin American Symposium on Theoretical Informatics
Abbreviated titleLATIN 2020
CitySao Paulo


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