Concept understanding — Adjoint and Inverse of a Matrix
For a square matrix A=[aij] of order n, the cofactor of aij is the signed minor Aij=(−1)i+jMij (the minor Mij is the determinant left after deleting row i and column j). Replace every entry of A by its cofactor to get the cofactor matrix; its transpose is the adjoint, adjA.
Theorem (the central identity). For every square matrix A of order n,
A(adjA)=(adjA)A=∣A∣In.
This follows from Laplace expansion: a row's entries dotted with their own cofactors reproduce ∣A∣, while a row's entries dotted with a different row's cofactors always give 0 -- so the product matrix is ∣A∣ on the diagonal and 0 off it.
Definition of the inverse. A square matrix B with AB=BA=In is called the inverse of A, written A−1. The inverse, when it exists, is unique. A−1 exists if and only if A is non-singular (∣A∣=0): dividing the central identity by ∣A∣ (possible exactly when ∣A∣=0) gives the working formula
A−1=∣A∣1adjA.
A singular matrix (∣A∣=0) has no inverse.
Worked illustration (order 2). For A=(acbd), the cofactors are A11=d,A12=−c,A21=−b,A22=a, so adjA=(d−c−ba) (swap the diagonal entries, negate the off-diagonal ones) and A−1=ad−bc1(d−c−ba) whenever ad−bc=0.
Standing laws of inverses (for non-singular A,B of the same order, λ=0 a scalar):
∣A−1∣=∣A∣1.
(AT)−1=(A−1)T.
(λA)−1=λ1A−1.
Left/right cancellation:AB=AC⇒B=C; BA=CA⇒B=C (pre/post-multiply by A−1) -- this fails when A is singular.
Reversal law:(AB)−1=B−1A−1 (note the order flips, exactly as for transposes).
Double inverse:(A−1)−1=A.
Six adjoint identities (non-singular A, order n): (i) adj(A−1)=(adjA)−1=∣A∣A; (ii) ∣adjA∣=∣A∣n−1; (iii) adj(adjA)=∣A∣n−2A; (iv) adj(λA)=λn−1adjA; (v) ∣adj(adjA)∣=∣A∣(n−1)2; (vi) (adjA)T=adj(AT); and for two non-singular matrices of the same order, adj(AB)=(adjB)(adjA) (order reverses, exactly like the inverse and the transpose).
Note
For a non-singular matrix of order 3, since ∣adjA∣=∣A∣2>0, one can also write A=±adjA1adj(adjA) -- useful when only adjA is given and A itself must be recovered.
Orthogonal matrices. A square matrix A is orthogonal if AAT=ATA=I, equivalently A is non-singular and A−1=AT. The standard rotation matrix W=(cosθsinθ−sinθcosθ) that converts one 2-D coordinate system into another (rotated by θ) is orthogonal, since W−1=WT is exactly the inverse rotation by −θ.
Cryptography via a non-singular matrix. Assign each letter A--Z a number 1--26 and a blank space 0. Group the plaintext numbers into row vectors of length n (padding with 0s if needed) and multiply each by a chosen non-singular encoding matrixE of order n (post-multiplication: (row)×E) to get the coded row. The receiver recovers the plaintext by post-multiplying each coded row by the decoding matrixE−1, since (row)E⋅E−1=row. The security of the scheme rests entirely on E being invertible and known only to sender and receiver.
For a 2×2 matrix swap the diagonal and negate the off-diagonal; for a 3×3 matrix, transpose the cofactor matrix (and for a scalar multiple kM, use adj(kM)=kn−1adjM).
(i) is a direct 2×2 swap-and-negate.
(ii) needs all nine 3×3 cofactors.
(iii) is 31 times a 3×3 matrix, so its adjoint scales by (31)2.
✓Final answer
adjA=(2−6−4−3);
adjA=1−3911−5−11−1;
adjA=323231−32313231−3232.
We find each adjoint as the transpose of the cofactor matrix; for the 2×2 case this collapses to the familiar swap-and-negate shortcut, and for the scalar-multiple case (iii) we save work with the scaling law adj(kM)=kn−1adjM.
Step 1. Part (i): swap-and-negate rule for a 2×2 matrix. For A=(acbd), adjA=(d−c−ba). With A=(−3642): adjA=(2−6−4−3).
Step 3. Part (ii): transpose the cofactor matrix. The cofactor matrix is 11−1−3119−5−1, so adjA is its transpose: adjA=1−3911−5−11−1.
Step 4. Part (iii): use the scaling law instead of nine fresh cofactors. Write A=31M with M=2−2121−2122. Since adj(kM)=kn−1adjM for an n×n matrix (here n=3,k=31): adjA=(31)2adjM=91adjM.