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Exercise 4.5 · Q16

Q.The cost of 4 kg onion, 3 kg wheat and 2 kg rice is ₹ 60. The cost of 2 kg onion, 4 kg wheat and 6 kg rice is ₹ 90. The cost of 6 kg onion 2 kg wheat and 3 kg rice is ₹ 70. Find cost of each item per kg by matrix method.

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We model the three purchase scenarios as a system of three linear equations in three unknowns (price per kg of onion, wheat, rice). Solving this system using the matrix method (inverse of the coefficient matrix) gives the unique prices: onions ₹5/kg, wheat ₹8/kg, and rice ₹8/kg.

The problem gives us three different combinations of purchases and their total costs. Each combination is a linear equation where the unknowns are the per‑kg prices of onion (xx), wheat (yy), and rice (zz).

The matrix method is perfect here because it turns a messy system of equations into a clean multiplication problem: Ax=bA \mathbf{x} = \mathbf{b}, where AA holds the quantities, x\mathbf{x} holds the unknown prices, and b\mathbf{b} holds the total costs. If AA is invertible, we can find x\mathbf{x} directly as x=A−1b\mathbf{x} = A^{-1} \mathbf{b}.

Let’s set it up and solve.


  1. Write the equations from the problem statement

    Let the cost per kg of onion be xx, wheat be yy, and rice be zz (all in ₹).

    From the first purchase:

    4x+3y+2z=604x + 3y + 2z = 60

    From the second purchase:

    2x+4y+6z=902x + 4y + 6z = 90

    From the third purchase:

    6x+2y+3z=706x + 2y + 3z = 70

  2. Write the system in matrix form

[432246623][xyz]=[609070]\begin{bmatrix} 4 & 3 & 2 \\ 2 & 4 & 6 \\ 6 & 2 & 3 \end{bmatrix} \begin{bmatrix} x \\ y \\ z \end{bmatrix} = \begin{bmatrix} 60 \\ 90 \\ 70 \end{bmatrix}

So A=[432246623]A = \begin{bmatrix} 4 & 3 & 2 \\ 2 & 4 & 6 \\ 6 & 2 & 3 \end{bmatrix}, x=[xyz]\mathbf{x} = \begin{bmatrix} x \\ y \\ z \end{bmatrix}, b=[609070]\mathbf{b} = \begin{bmatrix} 60 \\ 90 \\ 70 \end{bmatrix}.

  1. Find the determinant of AA — we need to check if AA is invertible.

det⁡(A)=4∣4623∣−3∣2663∣+2∣2462∣\det(A) = 4 \begin{vmatrix} 4 & 6 \\ 2 & 3 \end{vmatrix} - 3 \begin{vmatrix} 2 & 6 \\ 6 & 3 \end{vmatrix} + 2 \begin{vmatrix} 2 & 4 \\ 6 & 2 \end{vmatrix}

Compute each minor:

  • ∣4623∣=(4)(3)−(6)(2)=12−12=0\begin{vmatrix} 4 & 6 \\ 2 & 3 \end{vmatrix} = (4)(3) - (6)(2) = 12 - 12 = 0
  • ∣2663∣=(2)(3)−(6)(6)=6−36=−30\begin{vmatrix} 2 & 6 \\ 6 & 3 \end{vmatrix} = (2)(3) - (6)(6) = 6 - 36 = -30
  • ∣2462∣=(2)(2)−(4)(6)=4−24=−20\begin{vmatrix} 2 & 4 \\ 6 & 2 \end{vmatrix} = (2)(2) - (4)(6) = 4 - 24 = -20

So

det⁡(A)=4(0)−3(−30)+2(−20)=0+90−40=50\det(A) = 4(0) - 3(-30) + 2(-20) = 0 + 90 - 40 = 50

Since det⁡(A)=50≠0\det(A) = 50 \neq 0, AA is invertible and the system has a unique solution.

  1. Find the adjoint of AA (the transpose of the cofactor matrix).

    First, compute all cofactors Cij=(−1)i+jMijC_{ij} = (-1)^{i+j} M_{ij}, where MijM_{ij} is the minor.

    • C11=+∣4623∣=0C_{11} = + \begin{vmatrix} 4 & 6 \\ 2 & 3 \end{vmatrix} = 0

    • C12=−∣2663∣=−(−30)=30C_{12} = - \begin{vmatrix} 2 & 6 \\ 6 & 3 \end{vmatrix} = -(-30) = 30

    • C13=+∣2462∣=−20C_{13} = + \begin{vmatrix} 2 & 4 \\ 6 & 2 \end{vmatrix} = -20

    • C21=−∣3223∣=−[(3)(3)−(2)(2)]=−(9−4)=−5C_{21} = - \begin{vmatrix} 3 & 2 \\ 2 & 3 \end{vmatrix} = -[(3)(3) - (2)(2)] = -(9 - 4) = -5

    • C22=+∣4263∣=(4)(3)−(2)(6)=12−12=0C_{22} = + \begin{vmatrix} 4 & 2 \\ 6 & 3 \end{vmatrix} = (4)(3) - (2)(6) = 12 - 12 = 0

    • C23=−∣4362∣=−[(4)(2)−(3)(6)]=−(8−18)=10C_{23} = - \begin{vmatrix} 4 & 3 \\ 6 & 2 \end{vmatrix} = -[(4)(2) - (3)(6)] = -(8 - 18) = 10

    • C31=+∣3246∣=(3)(6)−(2)(4)=18−8=10C_{31} = + \begin{vmatrix} 3 & 2 \\ 4 & 6 \end{vmatrix} = (3)(6) - (2)(4) = 18 - 8 = 10

    • C32=−∣4226∣=−[(4)(6)−(2)(2)]=−(24−4)=−20C_{32} = - \begin{vmatrix} 4 & 2 \\ 2 & 6 \end{vmatrix} = -[(4)(6) - (2)(2)] = -(24 - 4) = -20

    • C33=+∣4324∣=(4)(4)−(3)(2)=16−6=10C_{33} = + \begin{vmatrix} 4 & 3 \\ 2 & 4 \end{vmatrix} = (4)(4) - (3)(2) = 16 - 6 = 10

    The cofactor matrix is …

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