# A general optimization solver based on OP-to-MaxSAT reduction

arXiv: https://arxiv.org/abs/2604.21961 · HTML: https://mlsanity.com/paper/2604.21961/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 14 of its 73 references that are in the map.
- First posted on arXiv 23 Apr 2026.

## Builds on (most cited first, 14 of 14)

- [Evolutionary Optimization of Model Merging Recipes](https://mlsanity.com/paper/2403.13187/) (2024)
- [Combinatorial Optimization with Physics-Inspired Graph Neural Networks](https://mlsanity.com/paper/2107.01188/) (2021)
- [Meta-Learning-Based Deep Reinforcement Learning for Multiobjective Optimization Problems](https://mlsanity.com/paper/2105.02741/) (2021)
- [Distributed Constrained Combinatorial Optimization leveraging Hypergraph Neural Networks](https://mlsanity.com/paper/2311.09375/) (2023)
- [Analysis of the Frank-Wolfe Method for Convex Composite Optimization involving a Logarithmically-Homogeneous Barrier](https://mlsanity.com/paper/2010.08999/) (2020)
- [Simulated annealing based heuristic for multiple agile satellites scheduling under cloud coverage uncertainty](https://mlsanity.com/paper/2003.08363/) (2020)
- [Qubit Mapping and Routing via MaxSAT](https://mlsanity.com/paper/2208.13679/) (2022)
- [Accelerate Presolve in Large-Scale Linear Programming via Reinforcement Learning](https://mlsanity.com/paper/2310.11845/) (2023)
- [Free-Energy Machine for Combinatorial Optimization](https://mlsanity.com/paper/2412.09285/) (2024)
- [Hessian barrier algorithms for non-convex conic optimization](https://mlsanity.com/paper/2111.00100/) (2021)
- [Constrained Multi-objective Optimization with Deep Reinforcement Learning Assisted Operator Selection](https://mlsanity.com/paper/2402.12381/) (2024)
- [Constrained stochastic blackbox optimization using a progressive barrier and probabilistic estimates](https://mlsanity.com/paper/2011.04225/) (2020)
- [An Abstract Model for Branch and Cut](https://mlsanity.com/paper/2111.09907/) (2021)
- [A Customized Augmented Lagrangian Method for Block-Structured Integer Programming](https://mlsanity.com/paper/2406.19605/) (2024)

## Abstract (arXiv)

Optimization problems are fundamental in diverse fields, such as engineering, economics, and scientific computing. However, current algorithms are mostly designed for specific problem types and exhibit limited generality in solving multiple types of optimization problems. To enhance generality, we propose an automated reduction method named OP-to-MaxSAT reduction and a general optimization solver based on OP-to-MaxSAT reduction (GORED). GORED unifies the solving of multiple types of optimization problems by reducing the problems from optimization problems to MaxSAT instances in polynomial time and solving them using the state-of-the-art MaxSAT solver. The generality and solution quality of GORED are validated through experiments on 136 instances across 11 types of optimization problems. Experimental results demonstrate that GORED not only successfully solves a wide range of optimization problems but also yields solutions comparable in quality to those from existing methods, with no statistically significant differences observed. By introducing automated reduction, this work shifts the paradigm of optimization solvers from designing specialized algorithms for each problem type to employing a single algorithm for diverse problems. As a result, advances in this single algorithm can now drive progress in a wide range of optimization problems across various domains.