# Accelerating Generalized Benders Decomposition for Wireless Resource Allocation

arXiv: https://arxiv.org/abs/2003.01294 · HTML: https://mlsanity.com/paper/2003.01294/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Mar 2020.

## Built on by (most cited first, 5 of 5)

- [Taking the human out of decomposition-based optimization via artificial intelligence: Part II. Learning to initialize](https://mlsanity.com/paper/2310.07082/) (2023)
- [A Model-based GNN for Learning Precoding](https://mlsanity.com/paper/2212.00860/) (2022)
- [Accelerating Cutting-Plane Algorithms via Reinforcement Learning Surrogates](https://mlsanity.com/paper/2307.08816/) (2023)
- [Accelerating L-shaped Two-stage Stochastic SCUC with Learning Integrated Benders Decomposition](https://mlsanity.com/paper/2311.10835/) (2023)
- [Feasibility-Aware Imitation Learning for Benders Decomposition](https://mlsanity.com/paper/2604.04801/) (2026)

## Abstract (arXiv)

Generalized Benders decomposition (GBD) is a globally optimal algorithm for mixed integer nonlinear programming (MINLP) problems, which are NP-hard and can be widely found in the area of wireless resource allocation. The main idea of GBD is decomposing an MINLP problem into a primal problem and a master problem, which are iteratively solved until their solutions converge. However, a direct implementation of GBD is time- and memory-consuming. The main bottleneck is the high complexity of the master problem, which increases over the iterations. Therefore, we propose to leverage machine learning (ML) techniques to accelerate GBD aiming at decreasing the complexity of the master problem. Specifically, we utilize two different ML techniques, classification and regression, to deal with this acceleration task. In this way, a cut classifier and a cut regressor are learned, respectively, to distinguish between useful and useless cuts. Only useful cuts are added to the master problem and thus the complexity of the master problem is reduced. By using a resource allocation problem in device-to-device communication networks as an example, we validate that the proposed method can reduce the computational complexity of GBD without loss of optimality and has strong generalization ability. The proposed method is applicable for solving various MINLP problems in wireless networks since the designs are invariant for different problems.