MIT
Lecture 6: Singular Value Decomposition (SVD)
Science & MathMathematicsEngineeringElectrical Engineering
Om kurset
Description Singular Value Decomposition (SVD) is the primary topic of this lecture. Professor Strang explains and illustrates how the SVD separates a matrix into rank one pieces, and that those pieces come in order of importance. Summary Columns of V are orthonormal eigenvectors of A_T_A. Av = \(\sigma\)u gives orthonormal eigenvectors u of _AA_T. \(\sigma^2 =\) eigenvalue of A_T_A = eigenvalue of _AA_T \( \neq\) 0 A = (rotation)(stretching)(rotation) \(U\Sigma\)_V_T for every A Related section in textbook: I.8 Instructor: Prof. Gilbert Strang