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A Faster Exponential Time Algorithm for Bin Packing With a Constant Number of Bins via Additive Combinatorics

A faster exponential time algorithm for bin packing with a constant number of bins via additive combinatorics
Authors: Nederlof, Jesper; Pawlewicz, Jakub; Swennenhuis, Céline M. F.; Węgrzycki, Karol;

A Faster Exponential Time Algorithm for Bin Packing With a Constant Number of Bins via Additive Combinatorics

Abstract

In the Bin Packing problem one is given $n$ items with weights $w_1,\ldots,w_n$ and $m$ bins with capacities $c_1,\ldots,c_m$. The goal is to find a partition of the items into sets $S_1,\ldots,S_m$ such that $w(S_j) \leq c_j$ for every bin $j$, where $w(X)$ denotes $\sum_{i \in X}w_i$. Björklund, Husfeldt and Koivisto (SICOMP 2009) presented an $\mathcal{O}^\star(2^n)$ time algorithm for Bin Packing. In this paper, we show that for every $m \in \mathbf{N}$ there exists a constant $σ_m >0$ such that an instance of Bin Packing with $m$ bins can be solved in $\mathcal{O}(2^{(1-σ_m)n})$ randomized time. Before our work, such improved algorithms were not known even for $m$ equals $4$. A key step in our approach is the following new result in Littlewood-Offord theory on the additive combinatorics of subset sums: For every $δ>0$ there exists an $\varepsilon >0$ such that if $|\{ X\subseteq \{1,\ldots,n \} : w(X)=v \}| \geq 2^{(1-\varepsilon)n}$ for some $v$ then $|\{ w(X): X \subseteq \{1,\ldots,n\} \}|\leq 2^{δn}$.

SODA 2021; 45 pages; 4 figures

Keywords

FOS: Computer and information sciences, Combinatorial optimization, F.2, Randomized algorithms, 68Q25, Combinatorics in computer science, algorithms, Arithmetic combinatorics; higher degree uniformity, cs.DS, bin packing, Littlewood-Offord theory, Computer Science - Data Structures and Algorithms, additive combinatorics, Analysis of algorithms, Data Structures and Algorithms (cs.DS)

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
2
Average
Average
Average
Green
bronze