Fast and Accurate Proper Orthogonal Decomposition using Efficient Sampling and Iterative Techniques for Singular Value Decomposition

In this article, we propose a computationally efficient iterative algorithm for proper orthogonal decomposition (POD) using random sampling based techniques. algorithm, additional rows and columns are sampled merging technique is used to update the dominant POD modes in each iteration. We derive bounds spectral norm of error introduced by series operations. use an existing theorem get approximate measure quality subspaces obtained on convergence Results various datasets indicate that and/or approximated with excellent accuracy significant runtime improvement over computing truncated SVD. also method compute large matrices do not fit RAM algorithms.