Pandora's Problem with Nonobligatory Inspection: Optimal Structure and a PTAS
December 03, 2022 Β· Declared Dead Β· π Symposium on the Theory of Computing
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Hedyeh Beyhaghi, Linda Cai
arXiv ID
2212.01524
Category
cs.DS: Data Structures & Algorithms
Cross-listed
cs.GT
Citations
27
Venue
Symposium on the Theory of Computing
Last Checked
4 months ago
Abstract
Weitzman introduced Pandora's box problem as a mathematical model of sequential search with inspection costs, in which a searcher is allowed to select a prize from one of $n$ alternatives. Several decades later, Doval introduced a close version of the problem, where the searcher does not need to incur the inspection cost of an alternative, and can select it uninspected. Unlike the original problem, the optimal solution to the nonobligatory inspection variant is proved to need adaptivity, and by recent work of [FLL22], finding the optimal solution is NP-hard. Our first main result is a structural characterization of the optimal policy: We show there exists an optimal policy that follows only two different pre-determined orders of inspection, and transitions from one to the other at most once. Our second main result is a polynomial time approximation scheme (PTAS). Our proof involves a novel reduction to a framework developed by [FLX18], utilizing our optimal two-phase structure. Furthermore, we show Pandora's problem with nonobligatory inspection belongs to class NP, which by using the hardness result of [FLL22], settles the computational complexity class of the problem. Finally, we provide a tight 0.8 approximation and a novel proof for committing policies [BK19] (informally, the set of nonadaptive policies) for general classes of distributions, which was previously shown only for discrete and finite distributions [GMS08].
Community Contributions
Found the code? Know the venue? Think something is wrong? Let us know!
π Similar Papers
In the same crypt β Data Structures & Algorithms
π
π
The Cartographer
R.I.P.
π»
Ghosted
Route Planning in Transportation Networks
R.I.P.
π»
Ghosted
Near-linear time approximation algorithms for optimal transport via Sinkhorn iteration
R.I.P.
π»
Ghosted
Hierarchical Clustering: Objective Functions and Algorithms
R.I.P.
π»
Ghosted
Graph Isomorphism in Quasipolynomial Time
π
π
The Cartographer
Simulation optimization: A review of algorithms and applications
Died the same way β π» Ghosted
R.I.P.
π»
Ghosted
Federated Learning: Strategies for Improving Communication Efficiency
R.I.P.
π»
Ghosted
In-Datacenter Performance Analysis of a Tensor Processing Unit
R.I.P.
π»
Ghosted
Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning
R.I.P.
π»
Ghosted