Prophet Secretary Against the Online Optimal

TL;DR

This paper introduces a polynomial-time approximation scheme for the prophet secretary problem, achieving near-optimal expected rewards against the online optimal benchmark by innovative discretization and decision-reduction techniques.

Contribution

It develops a PTAS for the prophet secretary problem against the online optimal, improving computational efficiency and decision-making strategies.

Findings

Achieved a PTAS for the prophet secretary problem.

Reduced dependence on input size in the approximation scheme.

Introduced a novel frontloading technique for decision reduction.

Abstract

We study the prophet secretary problem, a well-studied variant of the classic prophet inequality, where values are drawn from independent known distributions but arrive in uniformly random order. Upon seeing a value at each step, the decision-maker has to either select it and stop or irrevocably discard it. Traditionally, the chosen benchmark is the expected reward of the prophet, who knows all the values in advance and can always select the maximum one. %% In this work, we study the prophet secretary problem against a less pessimistic but equally well-motivated benchmark; the \emph{online} optimal. Here, the main goal is to find polynomial-time algorithms that guarantee near-optimal expected reward. As a warm-up, we present a quasi-polynomial time approximation scheme (QPTAS) achieving a $(1-\e)$-approximation in $O(n^{\text{poly} \log n\cdot f(\e)})$ time through careful discretization and non-trivial bundling processes. Using the toolbox developed for the QPTAS, coupled with a novel \emph{frontloading} technique that enables us to reduce the number of decisions we need to make, we are able to remove the dependence on $n$ in the exponent and obtain a polynomial time approximation scheme (PTAS) for this problem.