# Lower Bound of Matrix Multiplication

I am reading the textbook algorithms by S. Dasgupta, C.H. Papadimitriou, and U.V. Vazirani

The authors state in page $67$:

The preceding formula implies an $O(n^3)$ algorithm for matrix multiplication: there are $n^2$ entries to be computed, and each takes $O(n)$ time. For quite a while, this was widely believed to be the best running time possible, and it was even proved that in certain models of computation no algorithm could do better. It was therefore a source of great excitement when in $1969$, the German mathematician Volker Strassen announced a significantly more efficient algorithm, based upon divide-and-conquer.

The statement that interests my more in this quotation, is:

it was even proved that in certain models of computation no algorithm could do better.

Could anyone support me with some references where I can find a proof of that kind?

Strassen, in his paper describing Strassen's algorithm (Gaussian elimination is not optimal) mentions

the result of Klyuyev and Kokovkin-Shcherbak [1] that Gaussian elimination for solving a system of linear equations is optimal if one restricts oneself to operations upon rows and columns as a whole.

The reference is to

1. Klyuyev, V. V., and N. I. Kokovkin-Shcherbak: On the minimizations of the number of arithmetic operations for the solution of linear algebraic systems of equations. Translation by G. I. Tee: Technical Report CS 24, June 14, 1965, Computer Science Dept., Stanford University.

The paper is available online.

In modern literature this result is virtually unknown, and I am not aware of any other lower bound on matrix multiplication beyond the $\Omega(n^2)$ lower bounds of Bshouty and Shpilka and the $\Omega(n^2\log n)$ lower bound of Raz.

• I think, it is not bad to mention that latter lower bound is proved for matrices with real/complex numbers (which cannot be truly represented on computers) Aug 24, 2017 at 1:15
• Right, it's for the algebraic complexity model. Aug 24, 2017 at 6:38