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Complexity of Approximating Holder Classes from Information with Varying Gaussian Noise

Offered By: Banach Center via YouTube

Tags

Approximation Theory Courses Information Theory Courses Mathematical Analysis Courses Computational Complexity Courses Functional Analysis Courses

Course Description

Overview

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Explore the intricacies of approximating Holder classes in a 35-minute conference talk presented at the 51st Conference on Applications of Mathematics. Delve into the complexities introduced by varying Gaussian noise in information processing as Leszek Plaskota from the Institute of Applied Mathematics and Mechanics at the University of Warsaw's Faculty of Mathematics, Informatics, and Mechanics shares insights on this challenging mathematical problem. Gain a deeper understanding of the computational challenges and theoretical considerations involved in approximating Holder classes under these dynamic noise conditions.

Syllabus

Complexity of approximating Holder classes from in-formation with varying Gaussian noise


Taught by

Banach Center

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