At Class-12 level, "matrix decomposition" refers to a neat and useful theorem: every square matrix can be written as the sum of a symmetric matrix and a skew-symmetric matrix, and this split is unique.
The theorem
For any square matrix A,
A=symmetric part P21(A+A′)+skew-symmetric part Q21(A−A′),
where A′ is the transpose of A.
Why each part is what we claim
Write P=21(A+A′) and Q=21(A−A′). Then clearly P+Q=A. Now transpose each:
P′=21(A+A′)′=21(A′+A)=P⇒P is symmetric,
Q′=21(A−A′)′=21(A′−A)=−Q⇒Q is skew-symmetric.
So the two halves genuinely are a symmetric matrix and a skew-symmetric matrix that add back to A.
Every square matrix can be uniquely expressed as the sum of a symmetric matrix and a skew-symmetric matrix using the formulas A=21(A+AT)+21(A−AT). For the given matrix, the symmetric part is 21(A+AT) and the skew-symmetric part is 21(A−AT).
Why This Works: The Symmetric + Skew-Symmetric Decomposition
Any square matrix A can be split into two special parts. The symmetric part is a matrix that equals its own transpose — it's "mirrored" across the main diagonal. The skew-symmetric part is a matrix whose transpose equals its negative — the off-diagonal entries are opposite in sign, and the diagonal entries are always zero.
The beauty is that you don't need to guess. There's a direct formula:
A=symmetric21(A+AT)+skew-symmetric21(A−AT)
Why does this work? If you take the transpose of 21(A+AT), you get 21(AT+A), which is the same matrix — so it's symmetric. And the transpose of 21(A−AT) is 21(AT−A)=−21(A−AT), which is exactly the condition for skew-symmetry.
Let's apply this to the given matrix.
Step 1: Write down the given matrix and find its transpose
We have:
A=2143−11122
The transpose AT is obtained by swapping rows and columns:
Method: Decomposing a square matrix into symmetric and skew-symmetric parts
Every square matrix can be written uniquely as the sum of a symmetric matrix and a skew-symmetric matrix. The construction uses the matrix and its transpose.
Why it's wrong: without it, (A+A′)+(A−A′)=2A, not A; the decomposition only works when each part is halved. Correct approach: use P=21(A+A′) and Q=21(A−A′).
Mistake 2: Swapping the roles — taking the symmetric part as A−A′.
Why it's wrong: A+A′ is symmetric and A−A′ is skew-symmetric; reversing them mislabels the parts. Correct approach: symmetric uses the sum, skew uses the difference. …
Same / Similar Concept — real previous-year questions on the same or a closely similar concept, not this exact question.
TG EAPCET 2026Set eng-2026-05-11-FN1 markMCQ
Q.The rank of the matrix
−4−3−2−10−112043−4
is
(A) 0
(B) 1
(C) 2
(D) 3
›Reveal solutionSolution
The rank of a matrix is the number of linearly independent rows (or columns). By row-reducing the given 3×4 matrix, we find exactly two non-zero rows, so the rank is 2.
The rank of a matrix tells you the dimension of the vector space spanned by its rows (or columns). For a 3×4 matrix, the maximum possible rank is 3, but it could be less if rows are linearly dependent. The cleanest way to find rank is to row-reduce to echelon form and count the number of non-zero rows — each non-zero row corresponds to a vector that cannot be written as a combination of the others.
Let’s work through it step by step.
Write the matrix and start row reduction.
The given matrix is
A=−4−3−2−10−112043−4.
We want to get zeros below the first pivot. The first pivot is −4 in row 1, column 1. To eliminate the first column entries in rows 2 and 3, we use row operations.
Eliminate below the first pivot.
Replace row 2 with 4R2−3R1 (this avoids fractions for now):
4R2=[−120812],
3R1=[−12−3312],
so 4R2−3R1=[0350].
Thus the new row 2 is [0350].
Replace row 3 with 2R3−R1:
2R3=[−4−20−8],
R1=[−4−114],
so 2R3−R1=[0−1−1−12].
The matrix now looks like:
−400−13−115−140−12.
Eliminate below the second pivot.
The second pivot is 3 in row 2, column 2. To clear the entry below it, replace row 3 with 3R3+R2:
3R3=[0−3−3−36],
R2=[0350],
so 3R3+R2=[002−36]. …
Q.If A=bcaa0a0bb and B=0bba0abca are two matrices such that AB=2132867510, then a2+b2+c2=
(A) 14
(B) 17
(C) 22
(D) 29
›Reveal solutionSolution
The key idea is to multiply the given matrices symbolically, equate the result to the known product matrix, and solve for a,b,c by comparing entries. The sum of squares is a2+b2+c2=14, so the correct option is (A).
We are given two 3×3 matrices A and B and their product AB. The entries of A and B contain the unknowns a,b,c. Instead of solving a full system of nine equations, we can multiply A and B symbolically and then match the result to the given matrix. This yields equations that determine a,b,c directly.
Write down the matrices clearly
A=bcaa0a0bb,B=0bba0abca
We know:
AB=2132867510
Compute the product AB entry by entry
The (i,j) entry of AB is the dot product of row i of A with column j of B.