Computational Thinking, Python, Linear Algebra, Linear Regression, Jupyter Notebook.
= [[1, 1000], [0.001, 1]] wA, vrA = scipy.linalg.eig(A) wB, vrB = scipy.linalg.eig(B) wA, wB (array([ 1.+0.j, 1.+0.j]), array([ 2.00000000e+00+0.j, -2.22044605e-16+0.j])) Condition Number of a Matrix Loose ends from last time Full vs Reduced Factorization SVD Diagrams from Trefethen: QR