Computational Mathematics and Statistics Seminar by Srinivas Eswar: Randomized Approaches for Optimal Experimental Design

Speaker: Srinivas  Eswar, Argonne National Laboratory

Title: Randomized Approaches for Optimal Experimental Design

Abstract: This work describes connections between optimal experiment design (OED) for PDE-based Bayesian linear inverse problems and the column subset selection problem (CSSP) in matrix approximation. We derive bounds, both lower and upper, for the D-optimality criterion via CSSP for the independent and colored noise cases. Additionally, we describe ways to interpolate "left-out" sensor data using the "selected" sensors along with the errors in the data completion process. We develop and analyze randomized algorithms which achieve these bounds. Finally, we experimentally verify these results on a model advection-diffusion problem.

 

Computational Mathematics and Statistics Seminar

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