Skip to content
Question

Q.If A=[−100010001]A = \begin{bmatrix} -1 & 0 & 0 \\ 0 & 1 & 0 \\ 0 & 0 & 1 \end{bmatrix}, then A−1A^{-1} is
(A) [−1000−1000−1]\begin{bmatrix} -1 & 0 & 0 \\ 0 & -1 & 0 \\ 0 & 0 & -1 \end{bmatrix}
(B) [1000−1000−1]\begin{bmatrix} 1 & 0 & 0 \\ 0 & -1 & 0 \\ 0 & 0 & -1 \end{bmatrix}
(C) [−1000−10001]\begin{bmatrix} -1 & 0 & 0 \\ 0 & -1 & 0 \\ 0 & 0 & 1 \end{bmatrix}
(D) [−100010001]\begin{bmatrix} -1 & 0 & 0 \\ 0 & 1 & 0 \\ 0 & 0 & 1 \end{bmatrix}

CBSECBSE Class XII Board 2025MCQ· 1mImportance★★★★★
✓ Free question

The inverse of a diagonal matrix is obtained by taking the reciprocal of each diagonal entry. Since AA is diagonal with entries −1,1,1-1, 1, 1, its inverse is the diagonal matrix with entries 1/(−1)=−11/(-1) = -1, 1/1=11/1 = 1, 1/1=11/1 = 1, which is exactly AA itself. So A−1=AA^{-1} = A, matching option (D).

The key insight here is that AA is a diagonal matrix — all non-diagonal entries are zero. For such matrices, inversion is beautifully simple: you just invert each diagonal element individually. No row operations, no cofactors, no fuss.

Why does this work? Think about what a diagonal matrix does when it multiplies a vector: it scales each coordinate independently by the corresponding diagonal entry. The inverse must undo that scaling, so it scales each coordinate by the reciprocal. If the original scaling factor is dd, the inverse scaling factor is 1/d1/d. That’s the whole story.

Let’s walk through it step by step.

  1. Identify the structure.

    A=[−100010001]A = \begin{bmatrix} -1 & 0 & 0 \\ 0 & 1 & 0 \\ 0 & 0 & 1 \end{bmatrix} is diagonal. Its diagonal entries are a11=−1a_{11} = -1, a22=1a_{22} = 1, a33=1a_{33} = 1.

  2. Apply the diagonal inverse rule.

    For a diagonal matrix D=diag(d1,d2,…,dn)D = \text{diag}(d_1, d_2, \dots, d_n), the inverse is D−1=diag(1/d1,1/d2,…,1/dn)D^{-1} = \text{diag}(1/d_1, 1/d_2, \dots, 1/d_n), provided no di=0d_i = 0. Here none are zero, so:

A−1=[1−10001100011]=[−100010001].A^{-1} = \begin{bmatrix} \frac{1}{-1} & 0 & 0 \\ 0 & \frac{1}{1} & 0 \\ 0 & 0 & \frac{1}{1} \end{bmatrix} = \begin{bmatrix} -1 & 0 & 0 \\ 0 & 1 & 0 \\ 0 & 0 & 1 \end{bmatrix}.

  1. Notice the result. The inverse turned out to be exactly the same as AA. That’s because each diagonal entry is its own reciprocal: (−1)−1=−1(-1)^{-1} = -1 and 1−1=11^{-1} = 1. So AA is an involutory matrix — a matrix that is its own inverse.
Watch out

A common mistake is to think that inverting a diagonal matrix means changing the sign of every entry, or taking the reciprocal of all entries (including zeros). Neither is correct. Only the diagonal entries matter, and you take the reciprocal — not the negative — unless the reciprocal happens to equal the negative, as with −1-1.

  1. Check against the options.
    • (A) has all −1-1 on the diagonal — that would be the inverse of diag(−1,−1,−1)\text{diag}(-1, -1, -1), not our AA.
    • (B) has 1,−1,−11, -1, -1 — that’s the inverse of diag(1,−1,−1)\text{diag}(1, -1, -1).
    • (C) has −1,−1,1-1, -1, 1 — that’s the inverse of diag(−1,−1,1)\text{diag}(-1, -1, 1).
    • (D) has −1,1,1-1, 1, 1 — exactly what we computed.
Tip

You can verify instantly by multiplying AA by itself: A2=IA^2 = I. Since A2=IA^2 = I, A−1=AA^{-1} = A by definition. That’s a quick sanity check that avoids any calculation.

✓Final answer

The correct option is (D), since A−1=[−100010001]A^{-1} = \begin{bmatrix} -1 & 0 & 0 \\ 0 & 1 & 0 \\ 0 & 0 & 1 \end{bmatrix}, which is AA itself.

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.