Round-Competitive Algorithms for Uncertainty Problems with Parallel Queries
Thomas Erlebach, Michael Hoffmann, Murilo S. de Lima (School of, Informatics, University of Leicester, United Kingdom)

TL;DR
This paper introduces a new parallel query model for uncertainty problems, providing algorithms with competitive bounds on the number of rounds needed, and establishes nearly optimal bounds for various problems.
Contribution
It proposes a novel parallel query model for uncertainty problems and offers algorithms with competitive bounds, along with matching lower bounds for key problems.
Findings
Algorithms with (2+ε)-approximate optimal rounds for subset minima
A 2-round-competitive algorithm for the i-th smallest element problem
A 2-round-competitive algorithm for sorting uncertain elements
Abstract
The area of computing with uncertainty considers problems where some information about the input elements is uncertain, but can be obtained using queries. For example, instead of the weight of an element, we may be given an interval that is guaranteed to contain the weight, and a query can be performed to reveal the weight. While previous work has considered models where queries are asked either sequentially (adaptive model) or all at once (non-adaptive model), and the goal is to minimize the number of queries that are needed to solve the given problem, we propose and study a new model where queries can be made in parallel in each round, and the goal is to minimize the number of query rounds. We use competitive analysis and present upper and lower bounds on the number of query rounds required by any algorithm in comparison with the optimal number of query rounds. Given a set of…
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Taxonomy
TopicsOptimization and Search Problems · Auction Theory and Applications · Risk and Portfolio Optimization
