A sparse matrix is a matrix in which most of the elements are zero.
No. A scalar matrix is a diagonal matrix whose main diagonal elements are the same. Only if the diagonal elements are all 1 is it an identity matrix.
A matrix that have one or more elements with value zero.
It will be a square matrix and, to that extent, it is diagonalisable. However, the diagonal elements need not be non-zero. It will be a square matrix and, to that extent, it is diagonalisable. However, the diagonal elements need not be non-zero. It will be a square matrix and, to that extent, it is diagonalisable. However, the diagonal elements need not be non-zero. It will be a square matrix and, to that extent, it is diagonalisable. However, the diagonal elements need not be non-zero.
It will either be a 1*23 row matrix or a 23*1 column matrix.
Reduced matrix is a matrix where the elements of the matrix is reduced by eliminating the elements in the row which its aim is to make an identity matrix.
A sparse matrix is a matrix in which most of the elements are zero.
No. A scalar matrix is a diagonal matrix whose main diagonal elements are the same. Only if the diagonal elements are all 1 is it an identity matrix.
what is the key element of skill matrix
A matrix.
A matrix that have one or more elements with value zero.
Elements.
Zero Matrix When all elements of a matrix are zero than the matrix is called zero matrix. Example: A=|0 0 0|
It will be a square matrix and, to that extent, it is diagonalisable. However, the diagonal elements need not be non-zero. It will be a square matrix and, to that extent, it is diagonalisable. However, the diagonal elements need not be non-zero. It will be a square matrix and, to that extent, it is diagonalisable. However, the diagonal elements need not be non-zero. It will be a square matrix and, to that extent, it is diagonalisable. However, the diagonal elements need not be non-zero.
Lower-triangular Matrix A square matrix A whose elements aij=0 for i
It will either be a 1*23 row matrix or a 23*1 column matrix.
You basically write a nested for loop (one for within another one), to copy the elements of the matrix to a new matrix.