Fast and Accurate Proper Orthogonal Decomposition using Efficient Sampling and Iterative Techniques for Singular Value Decomposition
For practitioners needing fast POD of large datasets, this method offers a computationally efficient alternative to truncated SVD.
The paper proposes an iterative algorithm for proper orthogonal decomposition (POD) that uses random sampling and merging to approximate dominant POD modes, achieving excellent accuracy with significant runtime improvement over truncated SVD, and also handles matrices larger than RAM.
In this paper, we propose a computationally efficient iterative algorithm for proper orthogonal decomposition (POD) using random sampling based techniques. In this algorithm, additional rows and columns are sampled and a merging technique is used to update the dominant POD modes in each iteration. We derive bounds for the spectral norm of the error introduced by a series of merging operations. We use an existing theorem to get an approximate measure of the quality of subspaces obtained on convergence of the iteration. Results on various datasets indicate that the POD modes and/or the subspaces are approximated with excellent accuracy with a significant runtime improvement over computing the truncated SVD. We also propose a method to compute the POD modes of large matrices that do not fit in the RAM using this iterative sampling and merging algorithms.