Q.For the matrix , verify that
You're viewing a preview — the full solution, concept, methods & PYQ mapping are locked.
Start your 14-day free trial to unlock the full solution →For any square matrix , the sum is always symmetric and the difference is always skew-symmetric. For , we compute (symmetric) and (skew-symmetric), verifying both properties.
The Core Idea
This problem is not about doing random matrix arithmetic — it's about seeing a beautiful structural fact. Every square matrix can be split into two special parts: a symmetric part and a skew-symmetric part. The symmetric part is and the skew-symmetric part is . Here, we're just checking the numerators before halving.
Why does this work? Because transposition flips the roles. When you add and , the off-diagonal entries add up to the same number in symmetric positions — that's exactly what symmetry means. When you subtract, the off-diagonals become negatives of each other, which is the definition of skew-symmetry.
For any square matrix :
- is always symmetric:
- is always skew-symmetric:
Step-by-Step Verification
1. Write down and find
We have:
The transpose swaps rows and columns:
Notice how the off-diagonal entries and have swapped places.
2. Compute
Add entry by entry:
Now check symmetry: a matrix is symmetric if it equals its own transpose. Look at the off-diagonals — both are . The transpose of this matrix is:
It's identical to the original. So is symmetric. Done.
You don't even need to compute the transpose explicitly. For a matrix, symmetry just means the top-right equals the bottom-left. Here , so it's symmetric.
3. Compute
Subtract entry by entry:
Now check skew-symmetry: a matrix is skew-symmetric if its transpose equals its negative. Take the transpose: …
Unlock everything free for 14 days
- Full step-by-step solutions
- Concept-first explanations
- Methods, shortcuts & mistakes
- PYQ mapping + timed mock tests
Full access for 14 days. No credit card required.