1 citations · 1 across the 2 of their papers we have counts for
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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…
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…
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…
Adaptive Sampling Strategies for Stochastic Optimization
Raghu Bollapragada, Richard Byrd, Jorge Nocedal
In this paper, we propose a stochastic optimization method that adaptively controls the sample size used in the computation of gradient approximations. Unlike other variance reduct…