matrix1 # matrix2;
matrix # scalar;
matrix # vector;
The elementwise multiplication operator (#) computes a new matrix with elements that are the products of the corresponding elements of matrix1 and matrix2.
For example, the following statements compute the matrix ab
, shown in Figure 25.19:
a = {1 2, 3 4}; b = {4 8, 0 5}; ab = a#b; print ab;
In addition to multiplying matrices that have the same dimensions, you can use the elementwise multiplication operator to multiply a matrix and a scalar.
When either argument is a scalar, each element in matrix is multiplied by the scalar value.
When you use the matrix # vector form, each row or column of the matrix is multiplied by a corresponding element of the vector.
If you multiply by an column vector, each row of the matrix is multiplied by the corresponding row of the vector.
If you multiply by a row vector, each column of the matrix is multiplied by the corresponding column of the vector.
For example, a matrix can be multiplied on either side by a , , , or matrix. The following statements multiply the matrix a
by a column vector and a row vector. The results are shown in Figure 25.20.
c = {10, 100}; /* column vector */ r = {10 100}; /* row vector */ ac = a#c; ar = a#r; print ac, ar;
Elementwise multiplication is mathematically equivalent to multiplying by a diagonal matrix. However, the elementwise operation is more effieicnt, as shown by the following statements:
A = j(5,5,1); v = 2:6; /* 1x5 row vector */ D = diag(v); /* 5x5 diagonal matrix */ /* multiply columns by constants */ B = A*D; /* less efficient */ B = v # A; /* more efficient */ /* multiply rows by constants */ C = D*A; /* less efficient */ C = A # v`; /* more efficient */
Elementwise multiplication is also known as the Schur or Hadamard product. Elementwise multiplication (which uses the # operator) should not be confused with matrix multiplication (which uses the * operator).
When an element of a matrix contains a missing value, the corresponding element of the product is also a missing value.