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Matrix multiplication is when you multiply the numbers inside different matricies.[topleft#1]Xtopleft#2=top left
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Scalar multiplication A number out side a matrix multiplies all parts of the matrix
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What is performing addition subtraction and scalar multiplication of matrices?
Matrix arithmetic
What is the difference between scalene and scalar triangles?
there is no difference
What type of matrix is a vector?
Vector matrix has both size and direction. There are different types of matrix namely the scalar matrix, the symmetric matrix, the square matrix and the column matrix.
What are all the symbols for multiplication?
There 3 to 4 symbols of multiplication depending on the condition.If you are using computer for calculation the only symbol of multiplication is *.If you are using calculator the symbol for multiplication is x.If you are solving equation in paper the symbol for multiplication b/w variables are mostly represent by a dot(.) or the place is left empty. A.B or AxB the answer will be same is scalar calculation but will be different on vector calculation. If use scalar calculation 'AB' will also represent multiplication b/w two variables. But it is only applicable on variables.If we use scalar number in equation like 3(4-3)*3=9; here '3(' is representing the multiplication b/w the number and the bracket. If there is any number before a bracket without any symbol then this will show that the number is multiplying by the bracket values.
Is eigenvalue applicable only to n x n matrices?
The answer is yes, and here's why: Remember that for the eigenvalues (k) and eigenvectors (v) of a matrix (M) the following holds: M.v = k*v, where "." denotes matrix multiplication. This operation is only defined if the number of columns in the first matrix is equal to the number of rows in the second, and the resulting matrix/vector will have as many rows as the first matrix, and as many columns as the second matrix. For example, if you have a 3 x 2 matrix and multiply with a 2 x 4 matrix, the result will be a 3 x 4 matrix. Applying this to the eigenvalue problem, where the second matrix is a vector, we see that if the matrix M is m x n and the vector is n x 1, the result will be an m x 1 vector. Clearly, this can never be a scalar multiple of the original vector.
