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The definition of matrix multiplication is that if C = AB for an n × m matrix A and an m × p matrix B, then C is an n × p matrix with entries = =. From this, a simple algorithm can be constructed which loops over the indices i from 1 through n and j from 1 through p, computing the above using a nested loop:
In theoretical computer science, the computational complexity of matrix multiplication dictates how quickly the operation of matrix multiplication can be performed. Matrix multiplication algorithms are a central subroutine in theoretical and numerical algorithms for numerical linear algebra and optimization, so finding the fastest algorithm for matrix multiplication is of major practical ...
The matrix multiplication algorithm that results from the definition requires, in the worst case, multiplications and additions of scalars to compute the product of two square n×n matrices.
Square root of 2, Newton's method (()) Euler's number, Binary splitting of the Taylor series ... Algorithm Complexity Matrix multiplication:
In mathematics and computer programming, exponentiating by squaring is a general method for fast computation of large positive integer powers of a number, or more generally of an element of a semigroup, like a polynomial or a square matrix. Some variants are commonly referred to as square-and-multiply algorithms or binary exponentiation.
In linear algebra, the Strassen algorithm, named after Volker Strassen, is an algorithm for matrix multiplication. It is faster than the standard matrix multiplication algorithm for large matrices, with a better asymptotic complexity , although the naive algorithm is often better for smaller matrices.
The Scalable Universal Matrix Multiplication Algorithm (SUMMA) [5] is a more practical algorithm that requires less workspace and overcomes the need for a square 2D grid. It is used by the ScaLAPACK , PLAPACK , and Elemental libraries.
A multiplication algorithm is an algorithm ... quarter square multiplication involved floor function; ... Matrix multiplication algorithm;