Linear Programming Based Near-Optimal Pricing for Laminar Bayesian Online Selection
Nima Anari, Rad Niazadeh, Amin Saberi, Ali Shameli

TL;DR
This paper develops a polynomial-time approximation scheme for laminar Bayesian online selection problems, enabling near-optimal pricing strategies under hierarchical constraints, with applications to prophet inequalities.
Contribution
It introduces the first PTAS for laminar Bayesian online selection with constant-depth laminar matroids and extends the approach to production-constrained problems.
Findings
Achieves a PTAS for laminar matroids with constant depth
Extends LP-based approach to production-constrained problems
Re-derives classic prophet inequalities using the new technique
Abstract
The Bayesian online selection problem aims to design a pricing scheme for a sequence of arriving buyers that maximizes the expected social welfare (or revenue) subject to different structural constraints. Inspired by applications with a hierarchy of service, this paper focuses on the cases where a laminar matroid characterizes the set of served buyers. We give the first Polynomial-Time Approximation Scheme (PTAS) for the problem when the laminar matroid has constant depth. Our approach is based on rounding the solution of a hierarchy of linear programming relaxations that approximate the optimum online solution with any degree of accuracy, plus a concentration argument showing that rounding incurs a small loss. We also study another variation, which we call the production-constrained problem. The allowable set of served buyers is characterized by a collection of production and shipping…
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Taxonomy
TopicsOptimization and Search Problems · Advanced Bandit Algorithms Research · Auction Theory and Applications
