
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
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)
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)
| 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). | 2 | |
| 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. | Average | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Average | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Average |
