# Adaptive Sampling Quasi-Newton Methods for Derivative-Free Stochastic Optimization

arXiv: https://arxiv.org/abs/1910.13516 · HTML: https://mlsanity.com/paper/1910.13516/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Oct 2019.

## Built on by (most cited first, 2 of 2)

- [Dynamic Anisotropic Smoothing for Noisy Derivative-Free Optimization](https://mlsanity.com/paper/2405.01731/) (2024)
- [Adaptive First- and Second-Order Algorithms for Large-Scale Machine Learning](https://mlsanity.com/paper/2111.14761/) (2021)

## Abstract (arXiv)

We consider stochastic zero-order optimization problems, which arise in settings from simulation optimization to reinforcement learning. We propose an adaptive sampling quasi-Newton method where we estimate the gradients of a stochastic function using finite differences within a common random number framework. We employ modified versions of a norm test and an inner product quasi-Newton test to control the sample sizes used in the stochastic approximations. We provide preliminary numerical experiments to illustrate potential performance benefits of the proposed method.