Treffer: A parameterized approximation scheme for the 2D-Knapsack problem with wide items

Title:
A parameterized approximation scheme for the 2D-Knapsack problem with wide items
Contributors:
Faculty of Mathematics, Informatics, and Mechanics [Warsaw] (MIMUW), Uniwersytet Warszawski [Polska] = University of Warsaw [Poland] = Université de Varsovie [Pologne] (UW), Ideas NCBR Sp. z o.o. (IDEAS NCBR), École normale supérieure de Lyon (ENS de Lyon), Université de Lyon, Aalto University
Publisher Information:
CCSD, 2023.
Publication Year:
2023
Collection:
collection:ENS-LYON
collection:UDL
collection:UNIV-LYON
Original Identifier:
ARXIV: 2307.10672
HAL: hal-04162879
Document Type:
E-Ressource preprint<br />Preprints<br />Working Papers
Language:
English
Relation:
info:eu-repo/semantics/altIdentifier/arxiv/2307.10672
Rights:
info:eu-repo/semantics/OpenAccess
Accession Number:
edshal.hal.04162879v2
Database:
HAL

Weitere Informationen

We study a natural geometric variant of the classic Knapsack problem called 2D-Knapsack: we are given a set of axis-parallel rectangles and a rectangular bounding box, and the goal is to pack as many of these rectangles inside the box without overlap. Naturally, this problem is NP-complete. Recently, Grandoni et al. [ESA'19] showed that it is also W[1]-hard when parameterized by the size $k$ of the sought packing, and they presented a parameterized approximation scheme (PAS) for the variant where we are allowed to rotate the rectangles by 90° before packing them into the box. Obtaining a PAS for the original 2D-Knapsack problem, without rotation, appears to be a challenging open question. In this work, we make progress towards this goal by showing a PAS under the following assumptions: 1. both the box and all the input rectangles have integral, polynomially bounded sidelengths; 2. every input rectangle is wide --- its width is greater than its height; and 3. the aspect ratio of the box is bounded by a constant.Our approximation scheme relies on a mix of various parameterized and approximation techniques, including color coding, rounding, and searching for a structured near-optimum packing using dynamic programming.