Matrix Factorizations: Singular Value Decomposition. Presented by Nik Clark MTH 421. Introduction. In the exciting world of numerical analysis, one may wonder “Why? Why do I study matrices and their factorizations?”
Matrix Factorizations:Singular Value Decomposition
Presented by Nik Clark
Some interesting ways to decompose a matrix
We’d like to more formally introduce you to Singular Value Decomposition (SVD) and some of its applications.
All matrices Amxn have a singular value decomposition.
A = 1u1vT1 +2u2vT2 + …+rurvTr ,
Where r = rank(A), and is defined to be the number of linearly independent columns of A.
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