Optimal $k$-Secretary with Logarithmic Memory

TL;DR

This paper presents a memory-efficient algorithm for the $k$-secretary problem that matches the optimal competitive ratio using only logarithmic memory, by reducing the problem to quantile estimation.

Contribution

It introduces a novel reduction from $k$-secretary to quantile estimation and provides a new quantile algorithm with $O(\log k)$ memory achieving near-optimal performance.

Findings

Achieves optimal competitive ratio with $O(\log k)$ memory.

Develops a quantile estimation algorithm with $O(\sqrt{k})$ error and $O(\log k)$ memory.

Provides an exact $k$-th largest element algorithm with $O(\sqrt{k})$ words.

Abstract

We study memory-bounded algorithms for the $k$-secretary problem. The algorithm of Kleinberg (SODA 2005) achieves an optimal competitive ratio of $1 - O(1/\sqrt{k})$, yet a straightforward implementation requires $\Omega(k)$ memory. Our main result is a $k$-secretary algorithm that matches the optimal competitive ratio using $O(\log k)$ words of memory. We prove this result by establishing a general reduction from $k$-secretary to (random-order) quantile estimation, the problem of finding the $k$-th largest element in a stream. We show that a quantile estimation algorithm with an $O(k^{\alpha})$ expected error (in terms of the rank) gives a $(1 - O(1/k^{1-\alpha}))$-competitive $k$-secretary algorithm with $O(1)$ extra words. We then introduce a new quantile estimation algorithm that achieves an $O(\sqrt{k})$ expected error bound using $O(\log k)$ memory. Of independent interest, we give a different algorithm that uses $O(\sqrt{k})$ words and finds the $k$-th largest element exactly with high probability, generalizing a result of Munro and Paterson (1980).