1 citations · 1 across the 4 of their papers we have counts for
4 papers
A Trust-Region Algorithm for Noisy Equality Constrained Optimization
Shigeng Sun, Jorge Nocedal
This paper introduces a modified Byrd-Omojokun (BO) trust region algorithm to address the challenges posed by noisy function and gradient evaluations. The original BO method was de…
Design Guidelines for Noise-Tolerant Optimization with Applications in Robust Design
Yuchen Lou, Shigeng Sun, Jorge Nocedal
The development of nonlinear optimization algorithms capable of performing reliably in the presence of noise has garnered considerable attention lately. This paper advocates for st…
Stochastic Ratios Tracking Algorithm for Large Scale Machine Learning Problems
Shigeng Sun, Yuchen Xie
Many machine learning applications and tasks rely on the stochastic gradient descent (SGD) algorithm and its variants. Effective step length selection is crucial for the success of…
A Trust Region Method for the Optimization of Noisy Functions
Shigeng Sun, Jorge Nocedal
Classical trust region methods were designed to solve problems in which function and gradient information are exact. This paper considers the case when there are bounded errors (or…