394 citations · 614 across the 9 of their papers we have counts for
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Adaptive Finite-Difference Interval Estimation for Noisy Derivative-Free Optimization
Hao-Jun Michael Shi, Yuchen Xie, Melody Qiming Xuan +1
A common approach for minimizing a smooth nonlinear function is to employ finite-difference approximations to the gradient. While this can be easily performed when no error is pres…
On the Numerical Performance of Derivative-Free Optimization Methods Based on Finite-Difference Approximations
Hao-Jun Michael Shi, Melody Qiming Xuan, Figen Oztoprak +1
The goal of this paper is to investigate an approach for derivative-free optimization that has not received sufficient attention in the literature and is yet one of the simplest to…
A Noise-Tolerant Quasi-Newton Algorithm for Unconstrained Optimization
Hao-Jun Michael Shi, Yuchen Xie, Richard Byrd +1
This paper describes an extension of the BFGS and L-BFGS methods for the minimization of a nonlinear function subject to errors. This work is motivated by applications that contain…
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…
A Primer on Coordinate Descent Algorithms
Hao-Jun Michael Shi, Shenyinying Tu, Yangyang Xu +1
This monograph presents a class of algorithms called coordinate descent algorithms for mathematicians, statisticians, and engineers outside the field of optimization. This particul…