Essential Concepts for JEE Main & Advanced

For JEE Main and JEE Advanced problems on determinants and matrices, quick identification of the correct property is often more important than long expansion.

1. High-Yield Adjoint Properties

Let AA be a square matrix of order nn.

  • adjA=An1|\operatorname{adj} A| = |A|^{n-1}
  • For a non-singular matrix, adj(adjA)=An2A\operatorname{adj}(\operatorname{adj} A) = |A|^{n-2}A
  • Therefore, adj(adjA)=A(n1)2|\operatorname{adj}(\operatorname{adj} A)| = |A|^{(n-1)^2}
  • adj(kA)=kn1adjA\operatorname{adj}(kA) = k^{n-1}\operatorname{adj} A
  • adj(AB)=(adjB)(adjA)\operatorname{adj}(AB) = (\operatorname{adj} B)(\operatorname{adj} A)
  • kA=knA|kA| = k^n|A|

2. Systems of Linear Equations

For a system AX=BAX=B:

  • Unique solution: A0|A| \neq 0
  • No solution: A=0|A| = 0 and at least one of Cramer's determinants is non-zero, or equivalently (adjA)BO(\operatorname{adj} A)B \neq O
  • Infinitely many solutions: A=0|A| = 0 and all the corresponding determinants vanish, or equivalently (adjA)B=O(\operatorname{adj} A)B = O

3. Homogeneous System

For AX=OAX=O:

  • Only trivial solution: A0|A| \neq 0
  • Non-trivial solutions exist: A=0|A| = 0

4. Cayley-Hamilton Theorem

Every square matrix satisfies its own characteristic equation. This is especially useful in JEE problems for:

  • finding higher powers of a matrix,
  • reducing polynomials in matrices,
  • expressing A1A^{-1} as a polynomial in AA.