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Using a naive lower bound and schoolbook matrix multiplication for the upper bound, one can straightforwardly conclude that {{math|2 ≤ ω ≤ 3}}. Whether {{math|1=ω = 2}} is a major open question in [[theoretical computer science]], and there is a line of research developing matrix multiplication algorithms to get improved bounds on {{math|ω}}.
All recent algorithms in this line of research use the ''laser method'', a generalization of the Coppersmith–Winograd algorithm, which was given by [[Don Coppersmith]] and [[Shmuel Winograd]] in 1990 and was the best matrix multiplication algorithm until 2010.<ref name="coppersmith">{{cite journal|doi=10.1016/S0747-7171(08)80013-2 |title=Matrix multiplication via arithmetic progressions |url=http://www.cs.umd.edu/~gasarch/TOPICS/ramsey/matrixmult.pdf |year=1990 |last1=Coppersmith |first1=Don |last2=Winograd |first2=Shmuel |journal=Journal of Symbolic Computation |volume=9|issue=3|pages=251|doi-access=free }}</ref> The conceptual idea of these algorithms is similar to Strassen's algorithm: a way is devised for multiplying two {{math|''k'' × ''k''}}-matrices with fewer than {{math|''k''<sup>3</sup>}} multiplications, and this technique is applied recursively. The laser method has limitations to its power, and cannot be used to show that {{math|ω < 2.3725}}.<ref name="afl14">{{Cite
=== Group theory reformulation of matrix multiplication algorithms ===
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=== Lower bounds for ω ===
There is a trivial lower bound of {{tmath|\omega \ge 2}}. Since any algorithm for multiplying two {{math|''n'' × ''n''}}-matrices has to process all {{math|2''n''<sup>2</sup>}} entries, there is a trivial asymptotic lower bound of {{math|Ω(''n''<sup>2</sup>)}} operations for any matrix multiplication algorithm. Thus {{tmath|2\le \omega < 2.37188}}. It is unknown whether {{tmath|\omega > 2}}. The best known lower bound for matrix-multiplication complexity is {{math|Ω(''n''<sup>2</sup> log(''n''))}}, for bounded coefficient [[Arithmetic circuit complexity|arithmetic circuits]] over the real or complex numbers, and is due to [[Ran Raz]].<ref>{{cite
The exponent ω is defined to be a [[Accumulation point|limit point]], in that it is the infimum of the exponent over all matrix multiplication algorithm. It is known that this limit point is not achieved. In other words, under the model of computation typically studied, there is no matrix multiplication algorithm that uses precisely {{math|O(''n''<sup>ω</sup>)}} operations; there must be an additional factor of {{math|''n''<sup>o(1)</sup>}}.<ref name="CW.81"/>
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