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. …
(a) Using A−1=∣A∣1adj(A) with ∣A∣=40, solving gives x1=1,x2=2,x3=−1. OR (b) Writing z=reiθ, the equation reduces to r=0 or r2=2,e4iθ=−1, giving z=0 and four roots z=2ei(π/4+kπ/2),k=0,1,2,3 — five solutions in all …
(a) Inverts the coefficient matrix via its adjoint and multiplies by the constant vector to solve the linear system; (b) converts to polar form to show the equation forces r=0 or a fixed nonzero modulus with four distinct arguments, five roots total. Both alternatives answered below.