When Can Nonconvex Optimization Problems be Solved with Gradient Descent? A Few Case Studies
Gradient descent and related algorithms are ubiquitously used to solve optimization problems arising in machine learning and signal processing. In many cases, these problems are nonconvex yet such simple algorithms are still effective. In an attempt to better understand this phenomenon, we study a n...
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| Language: | English |
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2020
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| Online Access: | https://doi.org/10.7916/d8-9v41-2n47 |