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Variants of the Randomized Kaczmarz Algorithm and its Applications
Anna Ma
UCSD
Abstract:
Nowadays, data is exploding at a faster rate than computer architectures can handle. For that reason, mathematical techniques to analyze large-scale data need be developed. Stochastic iterative algorithms have gained interest due to their low memory footprint and adaptability for large-scale data. In this talk, we will present the Randomized Kaczmarz algorithm for solving extremely large linear systems of the form Ax=y. In the spirit of large-scale data, this talk will act under the assumption that the entire data matrix A cannot be loaded into memory in a single instance. We consider different settings including when a only factorization of A is available, when A is missing information, and a time-varying model. We will also present applications of these Kaczmarz variants to problems in data science.
Tuesday, December 4, 2018
11:00AM AP&M 2402
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Philip E. Gill
Michael Holst
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Randolph E. Bank
Philip E. Gill
Michael Holst
Administrative Contact:
[ Click to Send Email ]
Anna Ma
UCSD
Abstract:
Nowadays, data is exploding at a faster rate than computer architectures can handle. For that reason, mathematical techniques to analyze large-scale data need be developed. Stochastic iterative algorithms have gained interest due to their low memory footprint and adaptability for large-scale data. In this talk, we will present the Randomized Kaczmarz algorithm for solving extremely large linear systems of the form Ax=y. In the spirit of large-scale data, this talk will act under the assumption that the entire data matrix A cannot be loaded into memory in a single instance. We consider different settings including when a only factorization of A is available, when A is missing information, and a time-varying model. We will also present applications of these Kaczmarz variants to problems in data science.
Tuesday, December 4, 2018
11:00AM AP&M 2402