SearcharxivSearch

arXiv subjects

Tingwei Hu

Publications and source records attributed to Tingwei Hu.

2 recordsLinked to original sources

Dense Subset Sum in Multi-Dimension

We study the additive structure of dense subset sum in multi-dimension, and use the structure to develop efficient algorithms for the dense subset sum problem. More precisely, given a set $A$ of $n$ vectors in the $d$-dimensional hyperrectangle $[N_1]\times [N_2]\times\cdots\times [N_d]$, we study the structure of $\mathcal{S}(A)$, which is the set of all subset sums of $A$. We focus on the dense regime of the problem where $n \gg \sqrt{\Phi}$ and $\Phi = N_1 \times \cdots \times N_d$. We show that for any constant $d\geq 1$, if $n \gg \sqrt{\Phi}$, then $\mathcal{S}(A)$ contains a long generalized progression in multi-dimension. If we further have that no non-trivial lattice can contain the majority of $A$, then $\mathcal{S}(A)$ contains all the integer points in the zonotope $\{x_1\vec{a}_1 + \cdots + x_n\vec{a}_n: o(1)\leq x_j \leq 1-o(1), x_j \in \mathbb{R}\}$. Compared to the previous results for $d \geq 2$, our result significantly reduces the density threshold and enlarges the region inside which all the integer points belong to $\mathcal{S}(A)$. Also, it matches the bound for the 1-dimensional case. Using our combinatorics result, we also develop an $\tilde{O}(n)$-time algorithm for the dense subset sum problem in multi-dimension.

cs.DS

Near-Tight Approximation Algorithms for Bottleneck Multiple Knapsack Problems

In the bottleneck multiple knapsack problem, we are given a set of items and a set of knapsacks, where each item has a profit and a weight, and each knapsack has a capacity. Our goal is to assign items to knapsacks so as to maximize the minimum profit received by any knapsack subject to the capacity constraint. When all knapsacks have identical capacity, we give a $(\frac{2}{3} - \varepsilon)$-approximation algorithm for any constant $\varepsilon > 0$. This result almost matches the $(\frac{2}{3} + \varepsilon)$ inapproximability bound for the bottleneck multiple subset sum problem (Caprara et al., 2000). When the knapsacks can have arbitrary capacities, we propose a $(\frac{1}{2} - \varepsilon)$-approximation algorithm for any constant $\varepsilon > 0$. We also prove a hardness bound of $(\frac{1}{2} + \varepsilon)$ for any constant $\varepsilon > 0$.

cs.DS