39 research outputs found
A DEIM Induced CUR Factorization
We derive a CUR matrix factorization based on the Discrete Empirical
Interpolation Method (DEIM). For a given matrix , such a factorization
provides a low rank approximate decomposition of the form ,
where and are subsets of the columns and rows of , and is
constructed to make a good approximation. Given a low-rank singular value
decomposition , the DEIM procedure uses and to
select the columns and rows of that form and . Through an error
analysis applicable to a general class of CUR factorizations, we show that the
accuracy tracks the optimal approximation error within a factor that depends on
the conditioning of submatrices of and . For large-scale problems,
and can be approximated using an incremental QR algorithm that makes one
pass through . Numerical examples illustrate the favorable performance of
the DEIM-CUR method, compared to CUR approximations based on leverage scores
Probabilistic upper bounds for the matrix two-norm
We develop probabilistic upper bounds for the matrix two-norm, the largest singular value. These bounds, which are true upper bounds with a user-chosen high probability, are derived with a number of different polynomials that implicitly arise in the Lanczos bidiagonalization process. Since these polynomials are adaptively generated, the bounds typically give very good results. They can be computed efficiently. Together with an approximation that is a guaranteed lower bound, this may result in a small probabilistic interval for the matrix norm of large matrices within a fraction of a second
Fast Approximated POD for a Flat Plate Benchmark with a Time Varying Angle of Attack
An approximate POD algorithm provides an empirical Galerkin approximation with guaranteed a priori lower bound on the required resolution. The snapshot ensemble is partitioned into several sub-ensembles. Cross correlations between these sub-ensembles are approximated in terms of a far smaller correlation matrix. Computational speedup is nearly linear in the number of partitions, up to a saturation that can be estimated a priori. The algorithm is particularly suitable for analyzing long transient trajectories of high dimensional simulations, but can be applied also for spatial partitioning and parallel processing of very high spatial dimension data. The algorithm is demonstrated using transient data from two simulations. First, a two dimensional simulation of the flow over a flat plate, as it transitions from AOA = 30° to a horizontal position and back. Second, a three dimensional simulation of a flat plate with aspect ratio two as it transitions from a horizontal position to AOA = 30°