# A new look at reweighted message passing

arXiv: https://arxiv.org/abs/1309.5655 · HTML: https://mlsanity.com/paper/1309.5655/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Sep 2013.

## Built on by (most cited first, 9 of 9)

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- [FastDOG: Fast Discrete Optimization on GPU](https://mlsanity.com/paper/2111.10270/) (2021)
- [Structured Prediction Problem Archive](https://mlsanity.com/paper/2202.03574/) (2022)
- [Super-Reparametrizations of Weighted CSPs: Properties and Optimization Perspective](https://mlsanity.com/paper/2201.02018/) (2022)
- [Convergence of Some Convex Message Passing Algorithms to a Fixed Point](https://mlsanity.com/paper/2403.07004/) (2024)
- [ReMAP: Lossless Residual Neural Lifting for Scalable MAP Inference in Arbitrary-Order Markov Random Fields](https://mlsanity.com/paper/2411.18954/) (2024)

## Abstract (arXiv)

We propose a new family of message passing techniques for MAP estimation in graphical models which we call em Sequential Reweighted Message Passing (SRMP). Special cases include well-known techniques such as em Min-Sum Diffusion (MSD) and a faster em Sequential Tree-Reweighted Message Passing (TRW-S). Importantly, our derivation is simpler than the original derivation of TRW-S, and does not involve a decomposition into trees. This allows easy generalizations. We present such a generalization for the case of higher-order graphical models, and test it on several real-world problems with promising results.