The Restricted Isometry Property (RIP) is a concept used to characterize matrices that are nearly orthonormal when operating on sparse vectors. It is used in the field of compressed sensing and has been shown that random Gaussian, Bernoulli, and partial Fourier matrices satisfy the RIP with number of measurements nearly linear in the sparsity level. Computing these constants is strongly NP-hard.
Stanford University
Fall 2022
This course dives into the use of randomness in algorithms and data structures, emphasizing the theoretical foundations of probabilistic analysis. Topics range from tail bounds, Markov chains, to randomized algorithms. The concepts are applied to machine learning, networking, and systems. Prerequisites indicate intermediate-level understanding required.
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