# SADAM: Stochastic Adam, A Stochastic Operator for First-Order Gradient-based Optimizer

arXiv: https://arxiv.org/abs/2205.10247 · HTML: https://mlsanity.com/paper/2205.10247/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 31 references that are in the map.
- First posted on arXiv 20 May 2022.

## Builds on (most cited first, 11 of 11)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Generating Sequences With Recurrent Neural Networks](https://mlsanity.com/paper/1308.0850/) (2013)
- [SAGA: A Fast Incremental Gradient Method With Support for Non-Strongly Convex Composite Objectives](https://mlsanity.com/paper/1407.0202/) (2014)
- [Momentum-Based Variance Reduction in Non-Convex SGD](https://mlsanity.com/paper/1905.10018/) (2019)
- [Katyusha: The First Direct Acceleration of Stochastic Gradient Methods](https://mlsanity.com/paper/1603.05953/) (2016)
- [Communication Complexity of Distributed Convex Learning and Optimization](https://mlsanity.com/paper/1506.01900/) (2015)
- [A Universal Catalyst for First-Order Optimization](https://mlsanity.com/paper/1506.02186/) (2015)
- [Improved SVRG for Non-Strongly-Convex or Sum-of-Non-Convex Objectives](https://mlsanity.com/paper/1506.01972/) (2015)
- [A General Analysis of the Convergence of ADMM](https://mlsanity.com/paper/1502.02009/) (2015)
- [Less than a Single Pass: Stochastically Controlled Stochastic Gradient Method](https://mlsanity.com/paper/1609.03261/) (2016)
- [Nonconvex Sparse Learning via Stochastic Optimization with Progressive Variance Reduction](https://mlsanity.com/paper/1605.02711/) (2016)

## Built on by (most cited first, 1 of 1)

- [HOME-3: High-Order Momentum Estimator with Third-Power Gradient for Convex and Smooth Nonconvex Optimization](https://mlsanity.com/paper/2505.11748/) (2025)

## Abstract (arXiv)

In this work, to efficiently help escape the stationary and saddle points, we propose, analyze, and generalize a stochastic strategy performed as an operator for a first-order gradient descent algorithm in order to increase the target accuracy and reduce time consumption. Unlike existing algorithms, the proposed stochastic the strategy does not require any batches and sampling techniques, enabling efficient implementation and maintaining the initial first-order optimizer's convergence rate, but provides an incomparable improvement of target accuracy when optimizing the target functions. In short, the proposed strategy is generalized, applied to Adam, and validated via the decomposition of biomedical signals using Deep Matrix Fitting and another four peer optimizers. The validation results show that the proposed random strategy can be easily generalized for first-order optimizers and efficiently improve the target accuracy.