# Matroidal Approximations of Independence Systems

## TL;DR

This paper explores a heuristic for approximating maximum weight bases in independence systems using matroidal structures, which can outperform classical greedy algorithms even with minimal prior knowledge.

## Contribution

It introduces a new heuristic based on inner matroids for independence systems, improving approximation guarantees over traditional methods.

## Key findings

- Heuristic can outperform greedy algorithms in worst-case scenarios.
- Performance improves even with no additional knowledge (${\mathcal O}={\mathcal I}$).
- Provides theoretical analysis of the heuristic's approximation guarantees.

## Abstract

Milgrom (2017) has proposed a heuristic for determining a maximum weight basis of an independence system ${\mathcal I}$ given that we want an approximation guarantee only for sets in a prescribed ${\mathcal O}\subseteq {\mathcal I}$. This ${\mathcal O}$ reflects prior knowledge of the designer about the location of the optimal basis. The heuristic is based on finding an `inner matroid', one contained in the independence system. We show that even in the case ${\mathcal O}={\mathcal I}$ of zero additional knowledge the worst-case performance of this new heuristic can be better than that of the classical greedy algorithm.