# BADM: Batch ADMM for Deep Learning

arXiv: https://arxiv.org/abs/2407.01640 · HTML: https://mlsanity.com/paper/2407.01640/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 33 references that are in the map.
- First posted on arXiv 30 Jun 2024.

## Builds on (most cited first, 20 of 20)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [Denoising Diffusion Implicit Models](https://mlsanity.com/paper/2010.02502/) (2020)
- [Generative Adversarial Networks](https://mlsanity.com/paper/2203.00667/) (2022)
- [Neural Message Passing for Quantum Chemistry](https://mlsanity.com/paper/1704.01212/) (2017)
- [Conditional Generative Adversarial Nets](https://mlsanity.com/paper/1411.1784/) (2014)
- [An overview of gradient descent optimization algorithms](https://mlsanity.com/paper/1609.04747/) (2016)
- [Pitfalls of Graph Neural Network Evaluation](https://mlsanity.com/paper/1811.05868/) (2018)
- [Symbolic Discovery of Optimization Algorithms](https://mlsanity.com/paper/2302.06675/) (2023)
- [Sensitivity and Generalization in Neural Networks: an Empirical Study](https://mlsanity.com/paper/1802.08760/) (2018)
- [Design Space for Graph Neural Networks](https://mlsanity.com/paper/2011.08843/) (2020)
- [Training Neural Networks Without Gradients: A Scalable ADMM Approach](https://mlsanity.com/paper/1605.02026/) (2016)
- [Federated Learning via Inexact ADMM](https://mlsanity.com/paper/2204.10607/) (2022)
- [Revisiting LSTM Networks for Semi-Supervised Text Classification via Mixed Objective Function](https://mlsanity.com/paper/2009.04007/) (2020)
- [ADMM for Efficient Deep Learning with Global Convergence](https://mlsanity.com/paper/1905.13611/) (2019)
- [On ADMM in Deep Learning: Convergence and Saturation-Avoidance](https://mlsanity.com/paper/1902.02060/) (2019)
- [Federated Reinforcement Learning for Resource Allocation in V2X Networks](https://mlsanity.com/paper/2310.09858/) (2023)
- [New Environment Adaptation with Few Shots for OFDM Receiver and mmWave Beamforming](https://mlsanity.com/paper/2310.12343/) (2023)
- [A Convergent ADMM Framework for Efficient Neural Network Training](https://mlsanity.com/paper/2112.11619/) (2021)

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

- [Evolution of Optimization Methods: Algorithms, Scenarios, and Evaluations](https://mlsanity.com/paper/2604.12968/) (2026)
- [Neural Network Training via Stochastic Alternating Minimization with Trainable Step Sizes](https://mlsanity.com/paper/2508.04193/) (2025)

## Abstract (arXiv)

Stochastic gradient descent-based algorithms are widely used for training deep neural networks but often suffer from slow convergence. To address the challenge, we leverage the framework of the alternating direction method of multipliers (ADMM) to develop a novel data-driven algorithm, called batch ADMM (BADM). The fundamental idea of the proposed algorithm is to split the training data into batches, which is further divided into sub-batches where primal and dual variables are updated to generate global parameters through aggregation. We evaluate the performance of BADM across various deep learning tasks, including graph modelling, computer vision, image generation, and natural language processing. Extensive numerical experiments demonstrate that BADM achieves faster convergence and superior testing accuracy compared to other state-of-the-art optimizers.