Searcharxiv⌕ Search

arXiv · 2610.20297

Simple, Faster (min, +) Matrix Multiplication and APSP

Abstract

In this note, we provide a deterministic algorithm that computes the $(\min, +)$-Matrix Product $C = A * B$ of two $N \times N$ matrices $A$ and $B$ with polynomially bounded integer entries in $\tilde{O}(N^{2.9964})$ time, improving upon the exponent of $O(N^{2.9995})$ time obtained in the recent breakthrough by Anthropic (Alman and Vassilevska Williams, 2026). Our algorithm uses a phased approach first proposed by Bringmann, Grandoni, Saha, and Vassilevska Williams in 2016. The phased approach produces an approximation $\tilde{C}$ of $C$ in truly subcubic time such that the number of remaining triples $(i,k,j): A_{i,k}+B_{k,j} < \tilde{C}(i,j)$ is truly subcubic. This provided strong evidence that a truly subcubic $(\min, +)$-Matrix Product and therefore, a truly subcubic algorithm for All Pairs Shortest Paths (APSP) may exist. However, to find the remaining triples in subcubic time, a sparse certificate is needed whose construction was not known. We use the thin matrix product theorem of the recent breakthrough to provide such a certificate. This gives a simple modular subcubic algorithm for APSP.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Barna Saha, Christopher Ye. 2026-10-14. Simple, Faster (min, +) Matrix Multiplication and APSP. https://arxiv.org/abs/2610.20297

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

Deterministic Volume Estimation of Truncated Hypercubes

We present a deterministic polynomial-time algorithm for estimating the volume of a hypercube intersected by a fixed number of constraints, where each constraint is the sum of univariate functions that are each nonnegative, monotone, and convex. Such constraints include knapsack and norm-ball constraints. The case of the unit hypercube truncated by a single linear constraint (halfspace) is already #P-hard. Given a fixed number of such constraints in a given dimension, together with an error parameter, our algorithm computes a multiplicative approximation of the volume of their intersection with the unit hypercube in time polynomial in the dimension, reciprocal error, input encoding length, and output encoding length.

cs.DS↗

A Shortest Augmenting Path Algorithm for Linear Matroid Parity

The matroid parity problem generalizes both graph matching and matroid intersection. Although the general version is intractable, Lovász (1981) developed a polynomial-time algorithm for the linear matroid parity problem, assuming the availability of matrix representations. Subsequently, Gabow and Stallmann (1986) presented an augmenting path algorithm, which has long been recognized as one of the fastest deterministic algorithms. Since shortest augmenting paths improved algorithms for graph matching (Micali & Vazirani, 1980) and linear matroid intersection (Cunningham, 1986), extending these techniques to linear matroid parity appears to be a natural progression. However, such an algorithm has remained elusive for four decades. We present the first shortest augmenting path algorithm for linear matroid parity. Our approach synthesizes the augmenting path algorithm of Gabow and Stallmann with the synchronized blossom formation of Micali and Vazirani, whose correctness was recently proven by Vazirani (2024). Our key technical contributions are threefold: (i) a linear-algebraic argument that bounds the lengths of shortest augmenting paths for linear matroid parity, which generalizes Cunningham's bound for linear matroid intersection; (ii) an a priori characterization of shortest search paths through lower bounds on their lengths; and (iii) an extension of the structural properties for graph matching established by Izumi, Kitamura, and Yamaguchi (2025) to the linear matroid parity setting. Our algorithm deterministically solves the linear matroid parity problem in ${\rm O}(nr^2\log r)$ time, where $n$ is the ground set size and $r$ is the matroid rank. By incorporating fast matrix multiplication, this complexity can be further reduced to ${\rm O}(nr^2)$. These results improve upon the long-standing deterministic bounds of ${\rm O}(nr^3)$ and ${\rm O}(nr^ω)$.

cs.DS↗

A 0.4584-approximation for DR-submodular maximization over down-closed convex sets

We obtain a $0.4584$-approximation for nonnegative, non-monotone, $L$-smooth DR-submodular maximization over compact convex down-closed subsets of the unit cube, improving the $4/9\approx0.4444$ ratio of Aggarwal's projection-based algorithm~\cite{aggarwal2026geometry}. A recovery map combining exponential and linear-fractional branches admits a cube-wide affine upper bound. The resulting continuous-time Frank--Wolfe-type algorithm with randomized output and its randomized discrete-time implementation both satisfy $\E F(\widehat x)\ge(0.4584-\varepsilon)\OPT-δLD^2$; the implementation uses polynomially many cube-wide value/full-gradient queries and ordinary linear maximization calls, where $D$ is the feasible diameter and $\varepsilon,δ>0$ are accuracy parameters. Our guarantee and Aggarwal's hardness bound~\cite{aggarwal2026geometry} place the optimal approximation ratio for cube-wide value/full-gradient queries and linear maximization, with additive error $δLD^2$, in $[0.4584,0.4705)$. Standard multilinear sampling and pipage rounding imply an expected $(0.4584-\varepsilon)$-approximation for nonnegative submodular set-function maximization under a matroid constraint \cite[Lemma B.3]{vondrak2013symmetry}.

cs.DS↗