Tight Vector Bin Packing with Few Small Items via Fast Exact Matching in Multigraphs

TL;DR

This paper presents a faster fixed-parameter algorithm for Vector Bin Packing parameterized by small items, using a novel reduction to exact matching in multigraphs and a specialized algorithm for solving it.

Contribution

It introduces an $O^*(2^k)$ time algorithm for Vector Bin Packing, improving over previous methods, and establishes a tight lower bound under SETH.

Findings

Achieved $O^*(2^k)$ time complexity for the problem.

Developed a variant of the Mulmuley-Vazirani-Vazirani matching algorithm.

Provided lower bounds indicating the optimality of the exponential base.

Abstract

We solve the Bin Packing problem in $O^*(2^k)$ time, where $k$ is the number of items less or equal to one third of the bin capacity. This parameter measures the distance from the polynomially solvable case of only large (i.e., greater than one third) items. Our algorithm is actually designed to work for a more general Vector Bin Packing problem, in which items are multidimensional vectors. We improve over the previous fastest $O^*(k! \cdot 4^k)$ time algorithm. Our algorithm works by reducing the problem to finding an exact weight perfect matching in a (multi-)graph with $O^*(2^k)$ edges, whose weights are integers of the order of $O^*(2^k)$. To solve the matching problem in the desired time, we give a variant of the classic Mulmuley-Vazirani-Vazirani algorithm with only a linear dependence on the edge weights and the number of edges, which may be of independent interest. Moreover, we give a tight lower bound, under the Strong Exponential Time Hypothesis (SETH), showing that the constant $2$ in the base of the exponent cannot be further improved for Vector Bin Packing. Our techniques also lead to improved algorithms for Vector Multiple Knapsack, Vector Bin Covering, and Perfect Matching with Hitting Constraints.