比利时vs摩洛哥足彩
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university of california san diego
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math 278c: optimization and data science
prof. lijun ding
ucsd (l2ding@ucsd.edu)
optimization for statistical learning with low dimensional structure: regularity and conditioning
abstract:
many statistical learning problems, where one aims to recover an underlying low-dimensional signal, are based on optimization, e.g., the linear programming approach for recovering a sparse vector. existing work often either overlooked the high computational cost in solving the optimization problem, or required case-specific algorithm and analysis -- especially for nonconvex problems. this talk addresses the above two issues from a unified perspective of conditioning. in particular, we show that once the sample size exceeds the intrinsic dimension of the signal, (1) a broad range of convex problems and a set of key nonsmooth nonconvex problems are well-conditioned, (2) well-conditioning, in turn, inspires new algorithms and ensures the efficiency of off-the-shelf optimization methods.
october 9, 2024
4:00 pm
zoom link: ucsd.zoom.us/j/94146420185?
meeting id: 941 4642 0185
password: 278cfa24
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