Motivated largely by technological developments that generate extremely large scientific and Internet data sets, recent years have witnessed exciting developments in the theory and practice of matrix algorithms. Particularly remarkable is the use of randomization—typically assumed to be a property of the input data due to, e.g., noise in the data generation mechanisms—as an algorithmic or computational resource for the development of improved algorithms for fundamental matrix problems such as matrix multiplication, least-squares (LS) approximation, low-rank matrix approximation, etc. From an applied perspective, RandNLA is a vital new tool for machine learning, statistics, and data analysis.
In this seminar we will aquire knowledge of the core concepts of RandNLA and develop its fundamental algorithms.
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