8 citations · 14 across the 5 of their papers we have counts for
10 papers
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…
Constrained Optimization in the Presence of Noise
Figen Oztoprak, Richard Byrd, Jorge Nocedal
The problem of interest is the minimization of a nonlinear function subject to nonlinear equality constraints using a sequential quadratic programming (SQP) method. The minimizatio…
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…
Constrained and Composite Optimization via Adaptive Sampling Methods
Yuchen Xie, Raghu Bollapragada, Richard Byrd +1
The motivation for this paper stems from the desire to develop an adaptive sampling method for solving constrained optimization problems in which the objective function is stochast…
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…
Analysis of the BFGS Method with Errors
Yuchen Xie, Richard Byrd, Jorge Nocedal
The classical convergence analysis of quasi-Newton methods assumes that the function and gradients employed at each iteration are exact. In this paper, we consider the case when th…