Step 1: Definition of a transpose.
For a square matrix \( A \), the transpose \( A' \) is obtained by interchanging the rows and columns of \( A \).
Step 2: Subtracting \( A' \) from \( A \).
The matrix \( A - A' \) satisfies the property: \[ (A - A')' = A' - A = -(A - A'). \] Thus, \( A - A' \) is equal to the negative of its transpose, which is the definition of a skew symmetric matrix.
Step 3: Conclusion.
For any square matrix \( A \), \( A - A' \) is always a skew symmetric matrix. {10pt}
List-I | List-II |
(A) Absolute maximum value | (I) 3 |
(B) Absolute minimum value | (II) 0 |
(C) Point of maxima | (III) -5 |
(D) Point of minima | (IV) 4 |