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Found 4 papers, 1 papers with code
Constrained and Composite Optimization via Adaptive Sampling Methods
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 stochastic and the constraints are deterministic.
A Noise-Tolerant Quasi-Newton Algorithm for Unconstrained Optimization
This paper describes an extension of the BFGS and L-BFGS methods for the minimization of a nonlinear function subject to errors.
Optimization and Control
Adaptive Sampling Strategies for Stochastic Optimization
In this paper, we propose a stochastic optimization method that adaptively controls the sample size used in the computation of gradient approximations.
Exact and Inexact Subsampled Newton Methods for Optimization
The paper studies the solution of stochastic optimization problems in which approximations to the gradient and Hessian are obtained through subsampling.
