TY - CHAP
T1 - Query Minimization Under Stochastic Uncertainty
AU - Chaplick, Steven
AU - Halldórsson, Magnús M.
AU - Lima, Murilo Santos de
AU - Tonoyan, Tigran
N1 - DBLP's bibliographic metadata records provided through http://dblp.org/search/publ/api are distributed under a Creative Commons CC0 1.0 Universal Public Domain Dedication. Although the bibliographic metadata records are provided consistent with CC0 1.0 Dedication, the content described by the metadata records is not. Content may be subject to copyright, rights of privacy, rights of publicity and other restrictions.
PY - 2020
Y1 - 2020
N2 - 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.
AB - 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.
U2 - 10.1007/978-3-030-61792-9_15
DO - 10.1007/978-3-030-61792-9_15
M3 - Chapter
SN - 978-3-030-61791-2
T3 - Lecture Notes in Computer Science
SP - 181
EP - 193
BT - Latin American Symposium on Theoretical Informatics
A2 - Kohayakawa, Yoshiharu
A2 - Miyazawa, Flávio Keidi
PB - Springer, Cham
T2 - 14th Latin American Symposium on Theoretical Informatics
Y2 - 5 January 2021 through 8 January 2021
ER -