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Recovery of functions of many variables via compressive sensing

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ASCW01 - Challenges in optimal recovery and hyperbolic cross approximation

The talk will report on the use of compressive sensing for the recovery of functions of many variables from sample values. We will cover trigonometric expansions as well as expansions in tensorized orthogonal polynomial systems and provide convergence rates in terms of the number of samples which avoid the curse of dimensionality. The technique can be used for the numerical solution of parametric operator equations.

This talk is part of the Isaac Newton Institute Seminar Series series.

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