Efficient Algorithms for Subdeterminant Maximization under Partition Matroids

Abstract

We consider the determinant maximization problem under partition constraints: Given an $n\times n$ PSD matrix A and a partition matroid $M$ on $[n]$, find a base $S$ of $M$ that maximizes $\det (A_{S,S})$. We give an $e^{O(k)}$-approximation algorithm to find such a set $S$, where $k$ is the rank of $M$. This improves upon the current $k^{O(k)}$-approximation, and matches the current $e^k$-estimation guarantee, up to $O(1)$ factors in the exponent. Our algorithm is based on rounding the geometric max-min relaxation due to Nikolov-Singh'2016, using a continuous potential-driven process, and several new structural and analytic properties of this relaxation.

arXiv commentUnder submission to SODA 2027. The authors used GPT-5.5 Pro during the development of this work to explore proof strategies, search for related literature, and assist with verification. GPT was not used in any part of the exposition