(a) If , find and use it to solve the following system of equations: , , .
OR
(b) If is a singular matrix, then find all values of x, where .
(a) If , find and use it to solve the following system of equations: , , .
OR
(b) If is a singular matrix, then find all values of x, where .
This is an OR question — attempt either Part (a) or Part (b) fully.
Part (a):
- For , , so exists.
- .
- The system's coefficient matrix is , so the system is with .
- Then .
- Hence .
Part (b): or
- A matrix is singular when its determinant is 0. Expanding gives , i.e. .
- Using and : .
- ; or (rejected).
- . Both and lie in , so or .
Marking Scheme
- 1Part (a) — 1 mark: correct value of and conclusion that A⁻¹ exists.
- 2Part (a) — 2 marks: correct cofactors/adjoint and .
- 3Part (a) — 2 marks: recognising the coefficient matrix is , computing and obtaining .
- 4Part (b) — 2 marks: correctly expanding the determinant to .
- 5Part (b) — 2 marks: correct simplification to using trigonometric identities.
- 6Part (b) — 1 mark: obtaining both and and rejecting .
Hint
For (a), compute and , then note the system's coefficient matrix is , so . For (b), expand the determinant, set it to 0, and simplify using and .
Quick Oral Answer
For (a), so exists; since the coefficient matrix is , gives . For (b), setting the determinant to 0 yields or , so or in .
Analysis & Explanation
This 5-mark question tests the matrix (inverse) method for a linear system and the determinant condition for a singular matrix — both core applications from the Determinants chapter.
Concept (a): . A key feature here is that the system's coefficient matrix equals Aᵀ, not A, so the system is and the solution is — students must notice this transpose relationship rather than blindly using A⁻¹B.
Concept (b): A matrix is singular exactly when . Expanding the determinant converts the condition into a trigonometric equation, factorised using and .
Exam trap: In (a), sign errors in the cofactors are common — verify with , or substitute the found (x, y, z) back into all three equations. In (b), after factoring , students must keep both valid roots and (giving and ) and reject only ; dropping the sin x = 1/2 root is the most common error.
Real-world relevance: Solving AX = B by matrix inversion underlies computer-graphics transformations, structural load analysis, and Leontief input–output economic models.
Common Mistakes
- 1Sign errors while computing cofactors, leading to a wrong adjoint/A⁻¹ — always verify with .
- 2In part (a), using directly without noticing the coefficient matrix is (the solution needs ).
- 3In part (b), dropping the valid root () and reporting only , or keeping the invalid root .
Interesting Facts
The adjoint (adjugate) method for matrix inversion was formalised by Arthur Cayley in his 1858 paper 'A Memoir on the Theory of Matrices', which also introduced the term 'matrix' itself.
Matrix inversion via the determinant/adjoint method is computationally expensive for large matrices ( via cofactors), which is why real-world software uses Gaussian elimination or LU decomposition instead — but the adjoint method remains the standard hand-calculation technique for CBSE board exams.
The singular matrix condition () is exactly the condition under which a system of linear equations either has no solution or infinitely many — a concept central to consistency analysis in linear algebra.
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Frequently Asked Questions
How do you know if exists?
A⁻¹ exists if and only if (A is non-singular). If , the matrix is singular and A⁻¹ does not exist.
Why use in part (b)?
This double-angle identity converts the mixed cos 2x and sin 3x terms into expressions of sin x alone, allowing the determinant equation to be factorised and solved for x.
How many values of x satisfy part (b)?
Two: and , both lying in the closed interval . They come from and ; the third algebraic root is rejected as it is outside .