Power iteration repeatedly multiplies a vector by a matrix A and normalizes it. The vector converges to the dominant eigenvector at a rate determined by the ratio |λ₂/λ₁| of the two largest eigenvalues. Watch the vector angle and Rayleigh quotient converge as iterations accumulate.