Faster 0-1-Knapsack via Near-Convex Min-Plus-Convolution

We revisit the classic 0-1-Knapsack problem, in which we are given $n$ items with their weights and profits as well as a weight budget $W$, and the goal is to find a subset of items of total weight at most $W$ that maximizes the total profit. We study pseudopolynomial-time algorithms parameterized by the largest profit of any item $p{\max}$, and the largest weight of any item $w{\max}$. Our main result are algorithms for 0-1-Knapsack running in time $\tilde{O}(n\,w\max\,p\max^{2/3})$ and $\tilde{O}(n\,p\max\,w\max^{2/3})$, improving upon an algorithm in time $O(n\,p\max\,w\max)$ by Pisinger [J. Algorithms '99]. In the regime $p\max \approx w\max \approx n$ (and $W \approx \mathrm{OPT} \approx n^2$) our algorithms are the first to break the cubic barrier $n^3$. To obtain our result, we give an efficient algorithm to compute the min-plus convolution of near-convex functions. More precisely, we say that a function $f \colon [n] \mapsto \mathbf{Z}$ is $\Delta$-near convex with $\Delta \geq 1$, if there is a convex function $\breve{f}$ such that $\breve{f}(i) \leq f(i) \leq \breve{f}(i) + \Delta$ for every $i$. We design an algorithm computing the min-plus convolution of two $\Delta$-near convex functions in time $\tilde{O}(n\Delta)$. This tool can replace the usage of the prediction technique of Bateni, Hajiaghayi, Seddighin and Stein [STOC '18] in all applications we are aware of, and we believe it has wider applicability.