Gradient-based optimization algorithms can be studied from the perspective of limiting ordinary differential equations (ODEs). Motivated by the fact that existing ODEs do not distinguish between two fundamentally different algorithms—Nesterov’s accelerated gradient method for strongly convex functions (NAG-) and Polyak’s heavy-ball method—we study an alternative limiting process that yields
- Award ID(s):
- 1653838
- NSF-PAR ID:
- 10219110
- Date Published:
- Journal Name:
- 2020 59th IEEE Conference on Decision and Control (CDC)
- Page Range / eLocation ID:
- 4237 to 4242
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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