571 citations · 729 across the 15 of their papers we have counts for
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math.OC2018
Derivative-Free Optimization of Noisy Functions via Quasi-Newton Methods
Albert S. Berahas, Richard H. Byrd, Jorge Nocedal
This paper presents a finite difference quasi-Newton method for the minimization of noisy functions. The method takes advantage of the scalability and power of BFGS updating, and e…
math.OC2018★ 58 cited
A Progressive Batching L-BFGS Method for Machine Learning
Raghu Bollapragada, Dheevatsa Mudigere, Jorge Nocedal +2
The standard L-BFGS method relies on gradient approximations that are not dominated by noise, so that search directions are descent directions, the line search is reliable, and qua…