Compact Course with Joel A. Tropp
Concentration inequalities and matrix computations
From 3 to 6 May 2021, the International Research Training Group "IGDK Munich - Graz" offers a live online compact course on "Concentration inequalities and matrix computations" with Professor Joel A. Tropp from the California Institute of Technology (Caltech), USA.
IGDK Compact Course 2021
The IGDK Compact Course "Concentration inequalities and matrix computations" is organized by the international research training group IGDK. Due to the time difference to California, the course takes place early evening from 18:30 - 20:00.
Joel A. Tropp is Steele Family Professor of Applied and Computational Mathematics at Caltech. His research centers on data science, applied mathematics, numerical algorithms, and random matrix theory.
Further, course information is available under IGDK Compact Course: Concentration inequalities and matrix computations.
Dates and topics
This live online course gives an introduction to the theory of concentration inequalities with some basic applications to matrix computations. The course assumes only a basic level of experience with probability, linear algebra, and matrix computations. It does not assume exposure to high-dimensional probability or randomized matrix computations. The course will consist of four 90-minute lectures:
Monday, 3 May, 18:30 - 20:00
Scalar concentration: Independent sum model. Markov's inequality, cumulant generating functions, Laplace transform method, Bernstein inequality. Application to trace estimation.
Tuesday, 4 May, 18:30 - 20:00
Matrix concentration: Independent sum model. Matrix cumulant generating functions, matrix Laplace transform method, matrix Bernstein inequality. Application to approximate matrix multiplication.
Wednesday, 5 May, 18:30 - 20:00
Subspace embeddings: Random projections. Oblivious subspace embeddings. Examples: Sampling, sign matrices, Gaussian matrices. Application to linear regression.
Thursday, 6 May, 18:30 - 20:00
Randomized SVD: The truncated SVD. Randomized SVD algorithm. Linear algebraic error bound. Probabilistic error bound. Randomized subspace iteration.
All participants receive a confirmation certificate for 6 credit hours. Please note: You must log in to the course using your full name for the certificate to be issued.
Please register by 3pm, 3rd May 2021 using our online form.
The login data for the course will be sent subsequently. Please log in to the course using your full name.