# Accelerating Matrix Diagonalization through Decision Transformers with Epsilon-Greedy Optimization

arXiv: https://arxiv.org/abs/2406.16191 · HTML: https://mlsanity.com/paper/2406.16191/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 27 references that are in the map.
- First posted on arXiv 23 Jun 2024.

## Builds on (most cited first, 8 of 8)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Sequence to Sequence Learning with Neural Networks](https://mlsanity.com/paper/1409.3215/) (2014)
- [Decision Transformer: Reinforcement Learning via Sequence Modeling](https://mlsanity.com/paper/2106.01345/) (2021)
- [Multi-Game Decision Transformers](https://mlsanity.com/paper/2205.15241/) (2022)
- [Prompting Decision Transformer for Few-Shot Policy Generalization](https://mlsanity.com/paper/2206.13499/) (2022)
- [Deep Contextual Multi-armed Bandits](https://mlsanity.com/paper/1807.09809/) (2018)
- [How Crucial is Transformer in Decision Transformer?](https://mlsanity.com/paper/2211.14655/) (2022)
- [Matrix Diagonalization as a Board Game: Teaching an Eigensolver the Fastest Path to Solution](https://mlsanity.com/paper/2306.10075/) (2023)

## Abstract (arXiv)

This paper introduces a novel framework for matrix diagonalization, recasting it as a sequential decision-making problem and applying the power of Decision Transformers (DTs). Our approach determines optimal pivot selection during diagonalization with the Jacobi algorithm, leading to significant speedups compared to the traditional max-element Jacobi method. To bolster robustness, we integrate an epsilon-greedy strategy, enabling success in scenarios where deterministic approaches fail. This work demonstrates the effectiveness of DTs in complex computational tasks and highlights the potential of reimagining mathematical operations through a machine learning lens. Furthermore, we establish the generalizability of our method by using transfer learning to diagonalize matrices of smaller sizes than those trained.