A PTAS for Packing Hypercubes into a Knapsack

TL;DR

This paper presents a polynomial-time approximation scheme (PTAS) for the d-dimensional hypercube knapsack problem, overcoming previous limitations in higher dimensions by introducing a new structural lemma and packing approach.

Contribution

The paper introduces a novel structural lemma enabling a PTAS for high-dimensional hypercube packing, extending beyond the known 2-D results and improving approximation techniques.

Findings

Established a PTAS for d-dimensional hypercube packing.

Developed a structural lemma transforming packings into structured forms.

Provided an efficient algorithm for a related high-dimensional strip packing variant.

Abstract

We study the d-dimensional hypercube knapsack problem where we are given a set of d-dimensional hypercubes with associated profits, and a knapsack which is a unit d-dimensional hypercube. The goal is to find an axis-aligned non-overlapping packing of a subset of hypercubes such that the profit of the packed hypercubes is maximized. For this problem, Harren (ICALP'06) gave an algorithm with an approximation ratio of (1+1/2^d+epsilon). For d=2, Jansen and Solis-Oba (IPCO'08) showed that the problem admits a polynomial-time approximation scheme (PTAS); Heydrich and Wiese (SODA'17) further improved the running time and gave an efficient polynomial-time approximation scheme (EPTAS). Both the results use structural properties of 2-D packing, which do not generalize to higher dimensions. For d>2, it remains open to obtain a PTAS, and in fact, there has been no improvement since Harren's result. We settle the problem by providing a PTAS. Our main technical contribution is a structural lemma which shows that any packing of hypercubes can be converted into another structured packing such that a high profitable subset of hypercubes is packed into a constant number of special hypercuboids, called V-Boxes and N-Boxes. As a side result, we give an almost optimal algorithm for a variant of the strip packing problem in higher dimensions. This might have applications for other multidimensional geometric packing problems.