When Are Nonconvex Optimization Problems Not Scary?
Nonconvex optimization is NP-hard, even the goal is to compute a local minimizer. In applied disciplines, however, nonconvex problems abound, and simple algorithms, such as gradient descent and alternating direction, are often surprisingly effective. The ability of simple algorithms to find high-qua...
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| Language: | English |
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2016
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| Online Access: | https://doi.org/10.7916/D8251J7H |