A Faster Deterministic Algorithm for Kidney Exchange via Representative Set

TL;DR

This paper introduces a new representative set technique that significantly speeds up deterministic algorithms for the Kidney Exchange Problem, reducing the exponential time complexity from 14.34^t to 6.855^t.

Contribution

The paper presents a novel representative set approach that improves the deterministic algorithm's efficiency for solving kidney exchange problems.

Findings

Deterministic algorithm now runs in O*(6.855^t) time.

Previous best deterministic algorithm ran in O*(14.34^t) time.

The new technique effectively reduces computational complexity for large instances.

Abstract

The Kidney Exchange Problem is a prominent challenge in healthcare and economics, arising in the context of organ transplantation. It has been extensively studied in artificial intelligence and optimization. In a kidney exchange, a set of donor-recipient pairs and altruistic donors are considered, with the goal of identifying a sequence of exchange -- comprising cycles or chains starting from altruistic donors -- such that each donor provides a kidney to the compatible recipient in the next donor-recipient pair. Due to constraints in medical resources, some limits are often imposed on the lengths of these cycles and chains. These exchanges create a network of transplants aimed at maximizing the total number, $t$, of successful transplants. Recently, this problem was deterministically solved in $O^*(14.34^t)$ time (IJCAI 2024). In this paper, we introduce the representative set technique for the Kidney Exchange Problem, showing that the problem can be deterministically solved in $O^*(6.855^t)$ time.