Concept understanding — Solving Linear Systems by Matrix Inversion
Package a system of n linear equations in n unknowns as AX=B, with A the n×ncoefficient matrix, X the column of unknowns, and B the column of constants. Matrix inversion method applies exactly when A is square and non-singular (∣A∣=0).
Derivation. Starting from AX=B, pre-multiply both sides by A−1:
A−1(AX)=A−1B⟹(A−1A)X=A−1B⟹X=A−1B.
Worked illustration. For 2x+y=8,x−y=1: A=(211−1), B=(81). Here ∣A∣=−2−1=−3=0, so A−1=−31(−1−1−12)=31(111−2). Then X=A−1B=31(111−2)(81)=31(96)=(32), i.e. x=3,y=2 -- check: 2(3)+2=8 and 3−2=1, both correct.
Practical shape for a 3×3 system. Write the three equations, read off A (rows = equations, columns = coefficients of x,y,z in that fixed order) and B, compute ∣A∣, then adjA (transpose of the cofactor matrix), then A−1=∣A∣1adjA, and finally multiply A−1B to read off x,y,z from the resulting column.
Word problems (rates, mixtures, work, prices) translate the same way: name each unknown quantity, write one linear equation per given condition, assemble AX=B, then solve by the formula above. A common variant asks you to find two unknown matrices A and C from a matrix equation like 2A−B=P,A−2B=Q: treat it as simultaneous matrix equations and eliminate one matrix exactly as you would eliminate a scalar unknown, using matrix addition/subtraction (never division) throughout.
Watch out
This method needs Asquare and non-singular. If ∣A∣=0 or the system has more equations than unknowns (or vice versa), matrix inversion cannot be used -- fall back on Gaussian elimination or the rank method instead.
Write the system as AX=B, form A−1=∣A∣adjA, then X=A−1B gives every unknown at once.
✓Final answer
x=−11,y=4;
x=2,y=−4;
x=2,y=3,z=4;
x=3,y=−2,z=1.
For each system we write it as AX=B, compute ∣A∣ and adjA, form A−1=∣A∣adjA, and finally X=A−1B gives the solution.
Step 1. Part (i): set up AX=B.2x+5y=−2,x+2y=−3⇒A=(2152),X=(xy),B=(−2−3).
∣A∣=2(2)−5(1)=4−5=−1=0, so A is invertible. For (acbd), adj=(d−c−ba)=(2−1−52), so A−1=−11(2−1−52)=(−215−2).