Differences between revisions 5 and 6
Revision 5 as of 2023-10-30 17:59:11
Size: 2876
Comment: Moved some content into a vector multiplication page
Revision 6 as of 2023-10-30 18:01:59
Size: 3078
Comment: Made notation consistent
Deletions are marked like this. Additions are marked like this.
Line 11: Line 11:
Matrices are multiplied '''non-commutatively'''. The ''m'' rows of A are multiplied by the ''p'' rows of B. A must be as ''tall'' as B is ''wide''. Matrices are multiplied '''non-commutatively'''. The ''m'' rows of '''''A''''' are multiplied by the ''p'' rows of '''''B'''''. '''''A''''' must be as ''tall'' as '''''B''''' is ''wide''.
Line 35: Line 35:
A cell in a matrix is expressed as C,,ij,, where `i` is a row index and `j` is a column index. A cell in a matrix is expressed as ''c,,ij,,'' where ''i'' is a row index and ''j'' is a column index.
Line 60: Line 60:
Row `i` of C is a linear combination of the columns of B. Row ''i'' of '''''C''''' is a linear combination of the columns of '''''B'''''.
Line 80: Line 80:
Column `j` of C is a linear combination of the rows of A. Column ''j'' of '''''C''''' is a linear combination of the rows of '''''A'''''.
Line 98: Line 98:
C can be evaluated as a summation of the columns of A by the rows of B. '''''C''''' can be evaluated as a summation of the columns of '''''A''''' by the rows of '''''B'''''.
Line 121: Line 121:
C can be evaluated block-wise. Suppose A and B are 20x20 matrices; they can be divided each into 10x10 quadrants. '''''C''''' can be evaluated block-wise. Suppose '''''A''''' and '''''B''''' are 20x20 matrices; they can be divided each into 10x10 quadrants.
Line 123: Line 123:
Using these matrices A and B as the building blocks: Using these matrices '''''A''''' and '''''B''''' as the building blocks:
Line 166: Line 166:
But if a specific block of the product is of interest, it can be solved as C^1^ = A^1^B^1^ + A^2^B^3^. But if a specific block of the product is of interest, it can be solved as '''''C^1^''''' = '''''A^1^B^1^''''' + '''''A^2^B^3^'''''.

Matrix Multiplication


Introduction

Matrices are multiplied non-commutatively. The m rows of A are multiplied by the p rows of B. A must be as tall as B is wide.

julia> A = [1 2
            0 0
            0 0]
3×2 Matrix{Int64}:
 1  2
 0  0
 0  0

julia> B = [1 0 0
            2 0 0]
2×3 Matrix{Int64}:
 1  0  0
 2  0  0

julia> A * B
3×3 Matrix{Int64}:
 5  0  0
 0  0  0
 0  0  0

A cell in a matrix is expressed as cij where i is a row index and j is a column index.


Computation

Cell-wise

In a multiplication of matrices A and B, cell cij of the new matrix C is the dot product of row Ai and column Bj.

julia> [1 2; 3 4] * [1 0; 0 1]
2×2 Matrix{Int64}:
 1  2
 3  4

Row-wise

Row i of C is a linear combination of the columns of B.

Solving for each row as:

row 1 = 1(column 1 of B) + 2(column 2 of B)
      = 1[1 0] + 2[0 1]
      = [1 0] + [0 2]
      = [1 2]

row 2 = 3(column 1 of B) 4(column 2 of B)
      = 3[1 0] + 4[0 1]
      = [3 0] + [0 4]
      = [3 4]

Column-wise

Column j of C is a linear combination of the rows of A.

Solving for each column as:

column 1 = 1(row 1 of A) + 0(row 2 of A)
         = 1[1 2] + 0
         = [1 2]

column 2 = 0(row 1 of A) + 1(row 2 of A)
         = 0 + 1[3 4]
         = [3 4]

Summation

C can be evaluated as a summation of the columns of A by the rows of B.

julia> [1; 2] * [1 0]
2×2 Matrix{Int64}:
 1  0
 2  0

julia> [3; 4] * [0 1]
2×2 Matrix{Int64}:
 0  3
 0  4

julia> [1; 2] * [1 0] + [3; 4] * [0 1]
2×2 Matrix{Int64}:
 1  3
 2  4

Block-wise

C can be evaluated block-wise. Suppose A and B are 20x20 matrices; they can be divided each into 10x10 quadrants.

Using these matrices A and B as the building blocks:

julia> A = [1 2; 3 4]
2×2 Matrix{Int64}:
 1  2
 3  4

julia> B = [1 0; 0 1]
2×2 Matrix{Int64}:
 1  0
 0  1

Much larger matrices may be composed of these building blocks.

julia> A_ = [A1 A2; A3 A4]
4×4 Matrix{Int64}:
 1  2  1  2
 3  4  3  4
 1  2  1  2
 3  4  3  4

julia> B_ = [B1 B2; B3 B4]
4×4 Matrix{Int64}:
 1  0  1  0
 0  1  0  1
 1  0  1  0
 0  1  0  1

The entire product could be computed, as in:

julia> A_ * B_
4×4 Matrix{Int64}:
 2  4  2  4
 6  8  6  8
 2  4  2  4
 6  8  6  8

But if a specific block of the product is of interest, it can be solved as C1 = A1B1 + A2B3.

julia> A1 * B1 + A2 * B3
2×2 Matrix{Int64}:
 2  4
 6  8


CategoryRicottone

LinearAlgebra/MatrixMultiplication (last edited 2026-01-21 16:22:47 by DominicRicottone)