Correct Answer - Option 2 : I only
Try to find out the eigen values by taking the example of matrix with rank 2. Then check if given statement follows from that or not.
Calculation:
A be (n x n) real valued square symmetric matrix of rank 2.
\(\mathop \sum \limits_{i = 1}^n \mathop \sum \limits_{j = 1}^n A_{ij}^2 = 50\),
which means, sum of square of all elements of A = 50.
Also, rank of A = 2 i.e. we have (n – 2) eigen values are 0.
So eigen values are in the form of a1, a2,0, 0, ……
Let us consider a matrix A = \(\left[ {\begin{array}{*{20}{c}}
{ - 5}&0\\
0&5
\end{array}} \right]\)
Here, for this eigen values are [-5, 5].
Take another example, B = \(\left[ {\begin{array}{*{20}{c}}
5&0&0\\
0&5&0\\
0&0&0
\end{array}} \right]\) and C = \(\left[ {\begin{array}{*{20}{c}}
6&0&0\\
0&{\surd 14}&0\\
0&0&0
\end{array}} \right]\)
Matrix B and C are of rank 2 and symmetric.
Eigen value for A = 5, 5, 0
And eigen value for B = \(6,\;\sqrt {14} ,\;0\)
As 0 and 5 are in the range of [-5, 5], So statement 1 is correct.
But eigen value with largest magnitude which is 6 for C must be strictly greater than 5 and in case of B largest value is 5 which is not strictly greater than 5, So, second statement is incorrect here.