{"title":"Improved hardness of approximation for Geometric Bin Packing","authors":"Arka Ray , Sai Sandeep","doi":"10.1016/j.ipl.2024.106552","DOIUrl":null,"url":null,"abstract":"<div><div>The Geometric Bin Packing (GBP) problem is a generalization of Bin Packing where the input is a set of <em>d</em>-dimensional rectangles, and the goal is to pack them into <em>d</em>-dimensional unit cubes efficiently. It is NP-hard to obtain a PTAS for the problem, even when <span><math><mi>d</mi><mo>=</mo><mn>2</mn></math></span>. For general <em>d</em>, the best-known approximation algorithm has an approximation guarantee that is exponential in <em>d</em>. In contrast, the best hardness of approximation is still a small constant inapproximability from the case when <span><math><mi>d</mi><mo>=</mo><mn>2</mn></math></span>. In this paper, we show that the problem cannot be approximated within a <span><math><msup><mrow><mi>d</mi></mrow><mrow><mn>1</mn><mo>−</mo><mi>ϵ</mi></mrow></msup></math></span> factor unless <span><math><mtext>NP</mtext><mo>=</mo><mtext>P</mtext></math></span>.</div><div>Recently, <em>d</em>-dimensional Vector Bin Packing, a problem closely related to the GBP, was shown to be hard to approximate within a <span><math><mi>Ω</mi><mo>(</mo><mi>log</mi><mo></mo><mi>d</mi><mo>)</mo></math></span> factor when <em>d</em> is a fixed constant, using a notion of Packing Dimension of set families. In this paper, we introduce a geometric analog of it, the Geometric Packing Dimension of set families. While we fall short of obtaining similar inapproximability results for the Geometric Bin Packing problem when <em>d</em> is fixed, we prove a couple of key properties of the Geometric Packing Dimension which highlight fundamental differences between Geometric Bin Packing and Vector Bin Packing.</div></div>","PeriodicalId":56290,"journal":{"name":"Information Processing Letters","volume":"189 ","pages":"Article 106552"},"PeriodicalIF":0.7000,"publicationDate":"2024-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Information Processing Letters","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0020019024000826","RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"COMPUTER SCIENCE, INFORMATION SYSTEMS","Score":null,"Total":0}
引用次数: 0
Abstract
The Geometric Bin Packing (GBP) problem is a generalization of Bin Packing where the input is a set of d-dimensional rectangles, and the goal is to pack them into d-dimensional unit cubes efficiently. It is NP-hard to obtain a PTAS for the problem, even when . For general d, the best-known approximation algorithm has an approximation guarantee that is exponential in d. In contrast, the best hardness of approximation is still a small constant inapproximability from the case when . In this paper, we show that the problem cannot be approximated within a factor unless .
Recently, d-dimensional Vector Bin Packing, a problem closely related to the GBP, was shown to be hard to approximate within a factor when d is a fixed constant, using a notion of Packing Dimension of set families. In this paper, we introduce a geometric analog of it, the Geometric Packing Dimension of set families. While we fall short of obtaining similar inapproximability results for the Geometric Bin Packing problem when d is fixed, we prove a couple of key properties of the Geometric Packing Dimension which highlight fundamental differences between Geometric Bin Packing and Vector Bin Packing.
期刊介绍:
Information Processing Letters invites submission of original research articles that focus on fundamental aspects of information processing and computing. This naturally includes work in the broadly understood field of theoretical computer science; although papers in all areas of scientific inquiry will be given consideration, provided that they describe research contributions credibly motivated by applications to computing and involve rigorous methodology. High quality experimental papers that address topics of sufficiently broad interest may also be considered.
Since its inception in 1971, Information Processing Letters has served as a forum for timely dissemination of short, concise and focused research contributions. Continuing with this tradition, and to expedite the reviewing process, manuscripts are generally limited in length to nine pages when they appear in print.