the math behind optimization magic

There is much magical math these days around L1, LASSO, compressed sensing, and so on. These methods are having huge impacts across many fields, especially where data-driven models reign. There were two talks at SCDA today about these matters. In the morning, Christian Müller (SCDA) spoke about TREX, which is his set of methods for identifying predictors in very sparse problems. He showed incredible performance on a set of toy problems, and value in real problems. In the afternoon, Eftychios Pnevmatikakis (SCDA) reviewed a paper that proves some results related to the conditions under which an optimization problem (minimize blah subject to foo) will return the true or correct answer. There was a lot of geometry and there were some crazy sets. Definitions of “descent cone” and “statistical dimension” were introductions, for me, to some real math.

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