The secretary returns

TL;DR

This paper extends the secretary problem to a model where each request arrives twice in a random order, achieving improved competitive ratios and success probabilities, and applies these results to matroid secretary problems.

Contribution

It introduces a new model with repeated arrivals, analyzes its secretary problem, and provides algorithms with provably optimal or improved competitive ratios.

Findings

Achieves a ~0.768 competitive ratio for the secretary problem with k=2.

Shows a success probability of at least 2/3 without prior knowledge.

Provides a 2-approximation algorithm for the matroid secretary problem in the new model.

Abstract

In the online random-arrival model, an algorithm receives a sequence of n requests that arrive in a random order. The algorithm is expected to make an irrevocable decision with regard to each request based only on the observed history. We consider the following natural extension of this model: each request arrives k times, and the arrival order is a random permutation of the kn arrivals; the algorithm is expected to make a decision regarding each request only upon its last arrival. We focus primarily on the case when k=2, which can also be interpreted as each request arriving at, and departing from the system, at a random time. We examine the secretary problem: the problem of selecting the best secretary when the secretaries are presented online according to a random permutation. We show that when each secretary arrives twice, we can achieve a competitive ratio of ~0.768 (compared to 1/e in the classical secretary problem), and that it is optimal. We also show that without any knowledge about the number of secretaries or their arrival times, we can still hire the best secretary with probability at least 2/3, in contrast to the impossibility of achieving a constant success probability in the classical setting. We extend our results to the matroid secretary problem, introduced by Babaioff et al., (2007) and show a simple algorithm that achieves a 2-approximation to the maximal weighted basis in the new model (for k=2). We show that this approximation factor can be improved in special cases of the matroid secretary problem; in particular, we give a 16/9-competitive algorithm for the returning edge-weighted bipartite matching problem.