Dr. Yangwen Zhang, Department of Mathematical Sciences, Carnegie Mellon University

Wei Gong, Associate Professor

An answer to an open question in the incremental SVD

9:00-10:00 October 28(Friday), Tencent Meeting ID: 626-335-051

Incremental singular value decomposition (SVD) was proposed by Brand to efficiently compute the SVD of a matrix. The algorithm needs to compute thousands or millions of orthogonal matrices and to multiply them together. However, the multiplications may corrode the orthogonality. Hence many reorthogonalizations are needed in practice. In [Linear Algebra and its Applications 415 (2006) 20–30], Brand asked “It is an open question how often this is necessary to guarantee a certain overall level of numerical precision; it does not change the overall complexity.” In this paper, we answer this question and the answer is we can avoid computing the large amount of those orthogonal matrices and hence the reorthogonalizations are not necessary by modifying his algorithm. We prove that the modification does not change the outcomes of the algorithm. Numerical experiments are presented to illustrate the performance of our modification.