# The computational complexity of matrix multiplication

I am looking for information about the computational complexity of matrix multiplication of rectangular matrices. Wikipedia states that the complexity of multiplying $A \in \mathbb{R}^{m \times n}$ by $B \in \mathbb{R}^{n \times p}$ is $O(mnp)$ (schoolbook multiplication).

I have a case where $m$ and $n$ are much smaller than $p$, and I was hoping to get better complexity than linear in $p$, on the expense of making the dependence on $m$ and $n$ worse than linear.

Any ideas?

Thanks.

Note: the reason I am hoping for it to be possible is because of the well known result of less than cubic dependence in $p$ if $m=n=p$ (when matrices are all squares).

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A (sequential) algorithm's complexity cannot be smaller than the size of its output. For your problem can you represent the input and output using space that is sublinear in p? –  Colin McQuillan Jun 30 '12 at 11:17
are the elements mostly nonzero or often zero? ie sparse? that certainly leads to various optimizations. also it seems like the SVD [singular value decomposition] might be relevant based on the current response referring to approximations. –  vzn Jul 3 '12 at 4:44

Classical work of Coppersmith shows that for some $\alpha > 0$, one can multiply an $n \times n^\alpha$ matrix with an $n^\alpha \times n$ matrix in $\tilde{O}(n^2)$ arithmetic operations. This is a crucial ingredient of Ryan Williams's recent celebrated result.
A different strand of work concentrates on multiplying matrices approximately. You can check this work by Magen and Zouzias. This is useful for handling really large matrices, say multiplying an $n \times N$ matrix and an $N \times n$ matrix, where $N \gg n$.