It is known that the multiplication of an $N \times M$ matrix with an $M
\times P$ matrix can be performed using fewer multiplications than what the
naive $NMP$ approach suggests. The most famous instance of this is Strassen's
algorithm for multiplying two $2\times 2$ matrices in 7 instead of 8
multiplications. This gives rise to the constraint satisfaction problem of fast
matrix multiplication, where a set of $R < NMP$ multiplication terms must be
chosen and combined such that they satisfy correctness constraints on the
output matrix. Despite its highly combinatorial nature, this problem has not
been exhaustively examined from that perspective, as evidenced for example by
the recent deep reinforcement learning approach of AlphaTensor. In this work,
we propose a simple yet novel Constraint Programming approach to find
non-commutative algorithms for fast matrix multiplication or provide proof of
infeasibility otherwise. We propose a set of symmetry-breaking constraints and
valid inequalities that are particularly helpful in proving infeasibility. On
the feasible side, we find that exploiting solver performance variability in
conjunction with a sparsity-based problem decomposition enables finding
solutions for larger (feasible) instances of fast matrix multiplication. Our
experimental results using CP Optimizer demonstrate that we can find fast
matrix multiplication algorithms for matrices up to $3\times 3$ in a short
amount of time.

本研究提出一种简单而新颖的基于约束编程的方法，以查找快速矩阵乘法的非交换算法或提供不可行性证明。实验结果表明，我们可以在短时间内找到 $3 	imes 3$ 的矩阵的快速矩阵乘法算法。

不流泪的快速矩阵乘法：一种约束编程方法

Fast Matrix Multiplication Without Tears: A Constraint Programming  Approach

In the context of Answer Set Programming, this paper investigates
symmetry-breaking to eliminate symmetric parts of the search space and,
thereby, simplify the solution process. We propose a reduction of disjunctive
logic programs to a coloured digraph such that permutational symmetries can be
constructed from graph automorphisms. Symmetries are then broken by introducing
symmetry-breaking constraints. For this purpose, we formulate a preprocessor
that integrates a graph automorphism system. Experiments demonstrate its
computational impact.

本篇研究在回答集规划（Answer Set Programming）背景下，探讨通过对称性破除来简化解决方案的过程，提出将不相干的逻辑程序转化为有色图的缩减方法，并通过图自同构构建置换对称性，然后通过引入对称性破除约束来破坏对称性。针对此目的，我们设计了一个包含图自同构系统的预处理器。实验证明该系统具有较高的计算效率。