Matching with Nested and Bundled Pandora Boxes

TL;DR

This paper introduces a novel approach to max-weighted matching with costs using nested Pandora's boxes, providing a constant-factor approximation algorithm and showing limitations of other natural algorithms.

Contribution

It develops a fully constructive optimal solution to the nested-boxes problem and applies it to matching, establishing a new method with provable approximation guarantees.

Findings

Edge-based algorithms are limited to o(1) approximation.

Vertex-based algorithms cannot achieve constant-factor approximation.

The nested-box approach yields a constant-factor approximation.

Abstract

We consider max-weighted matching with costs for learning the weights, modeled as a "Pandora's Box" on each endpoint of an edge. Each vertex has an initially-unknown value for being matched to a neighbor, and an algorithm must pay some cost to observe this value. The goal is to maximize the total matched value minus costs. Our model is inspired by two-sided settings, such as matching employees to employers. Importantly for such settings, we allow for negative values which cause existing approaches to fail. We first prove upper bounds for algorithms in two natural classes. Any algorithm that "bundles" the two Pandora boxes incident to an edge is an $o(1)$-approximation. Likewise, any "vertex-based" algorithm, which uses properties of the separate Pandora's boxes but does not consider the interaction of their value distributions, is an $o(1)$-approximation. Instead, we utilize Pandora's Nested-Box Problem, i.e. multiple stages of inspection. We give a self-contained, fully constructive optimal solution to the nested-boxes problem, which may have structural observations of interest compared to prior work. By interpreting each edge as a nested box, we leverage this solution to obtain a constant-factor approximation algorithm. Finally, we show any "edge-based" algorithm, which considers the interactions of values along an edge but not with the rest of the graph, is also an $o(1)$-approximation.