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Computational Science Seminar by Bosu Choi
Friday, Nov. 22, 2019
1 p.m. - 2 p.m. Location: SLC 2.302

 Bosu Choi

Oden Institute, UT Austin

Sparse Harmonic Transforms

In this talk, we will discuss the development of fast algorithms learning multivariate functions which are well approximated by the expansion of a few Bounded Orthonormal Product Basis (BOPB) functions. Specifically, two classes of function learning algorithms will be introduced. Considering the Fourier product basis, we will see several versions of sparse Fourier transforms (SFTs) and their error guarantees. Furthermore, considering the general BOPB functions, sublinear-time compressive sensing algorithms will be introduced. Such functions appear in many applications including, e.g., various Uncertainty Quantification (UQ) problems where the quantity of interest regarding the solution of parametric PDEs is approximately sparse in such basis functions. In detail, we will discuss a new variant of CoSaMP algorithm with a sublinear-time support identification satisfying ``Support Identification Property (SIP)". Any support identification procedure satisfying this property can produce a new version of CoSaMP algorithm. As well as the theoretical guarantees, numerical experiment results will be shown for both classes of algorithms. The sublinear-time CoSaMP can deal with the approximation of the functions in the spans of fairly general sets of as many as 10^230 orthonormal basis functions, and the SFT can deal with the approximation of the functions in the span of even more basis functions.


Persons with disabilities may submit a request for accommodations to participate in this event at UT Dallas' ADA website. You may also call (972) 883-5331 for assistance or send an email to [email protected]. All requests should be received no later than 2 business days prior to the event.
Contact Info:
John Zweck, 972-883-6699
Questions? Email me.

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