Recursive Decomposition for Nonconvex Optimization

8 Nov 2016 Abram L. Friesen Pedro Domingos

Continuous optimization is an important problem in many areas of AI, including vision, robotics, probabilistic inference, and machine learning. Unfortunately, most real-world optimization problems are nonconvex, causing standard convex techniques to find only local optima, even with extensions like random restarts and simulated annealing... (read more)

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