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That is, the entry of the product is obtained by multiplying term-by-term the entries of the i th row of A and the j th column of B, and summing these n products. In other words, c i j {\displaystyle c_{ij}} is the dot product of the i th row of A and the j th column of B .
To perform row reduction on a matrix, one uses a sequence of elementary row operations to modify the matrix until the lower left-hand corner of the matrix is filled with zeros, as much as possible. There are three types of elementary row operations: Swapping two rows, Multiplying a row by a nonzero number, Adding a multiple of one row to ...
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 linear algebra, a column vector with elements is an matrix [1] consisting of a single column of entries, for example, = [].. Similarly, a row vector is a matrix for some , consisting of a single row of entries, = […]. (Throughout this article, boldface is used for both row and column vectors.)
The outer product is equivalent to a matrix multiplication, provided that is represented as a column vector and as a column vector (which makes a row vector). [ 2 ] [ 3 ] For instance, if m = 4 {\displaystyle m=4} and n = 3 , {\displaystyle n=3,} then [ 4 ]
To use column-major order in a row-major environment, or vice versa, for whatever reason, one workaround is to assign non-conventional roles to the indexes (using the first index for the column and the second index for the row), and another is to bypass language syntax by explicitly computing positions in a one-dimensional array.
A row can be replaced by the sum of that row and a multiple of another row. R i + k R j → R i , where i ≠ j {\displaystyle R_{i}+kR_{j}\rightarrow R_{i},{\mbox{where }}i\neq j} If E is an elementary matrix, as described below, to apply the elementary row operation to a matrix A , one multiplies A by the elementary matrix on the left, EA .
The left column visualizes the calculations necessary to determine the result of a 2x2 matrix multiplication. Naïve matrix multiplication requires one multiplication for each "1" of the left column. Each of the other columns (M1-M7) represents a single one of the 7 multiplications in the Strassen algorithm.
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