Combinatorial Optimization
292 papers with code • 0 benchmarks • 2 datasets
Combinatorial Optimization is a category of problems which requires optimizing a function over a combination of discrete objects and the solutions are constrained. Examples include finding shortest paths in a graph, maximizing value in the Knapsack problem and finding boolean settings that satisfy a set of constraints. Many of these problems are NP-Hard, which means that no polynomial time solution can be developed for them. Instead, we can only produce approximations in polynomial time that are guaranteed to be some factor worse than the true optimal solution.
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Latest papers with no code
Deep Reinforcement Learning for Traveling Purchaser Problems
The traveling purchaser problem (TPP) is an important combinatorial optimization problem with broad applications.
Solving the QAP by Two-Stage Graph Pointer Networks and Reinforcement Learning
In this paper, we propose the deep reinforcement learning model called the two-stage graph pointer network (GPN) for solving QAP.
Self-Improved Learning for Scalable Neural Combinatorial Optimization
The end-to-end neural combinatorial optimization (NCO) method shows promising performance in solving complex combinatorial optimization problems without the need for expert design.
Leveraging Constraint Programming in a Deep Learning Approach for Dynamically Solving the Flexible Job-Shop Scheduling Problem
Recent advancements in the flexible job-shop scheduling problem (FJSSP) are primarily based on deep reinforcement learning (DRL) due to its ability to generate high-quality, real-time solutions.
Surrogate Assisted Monte Carlo Tree Search in Combinatorial Optimization
Industries frequently adjust their facilities network by opening new branches in promising areas and closing branches in areas where they expect low profits.
An Efficient Learning-based Solver Comparable to Metaheuristics for the Capacitated Arc Routing Problem
Recently, neural networks (NN) have made great strides in combinatorial optimization.
Graph Learning for Parameter Prediction of Quantum Approximate Optimization Algorithm
In recent years, quantum computing has emerged as a transformative force in the field of combinatorial optimization, offering novel approaches to tackling complex problems that have long challenged classical computational methods.
How Multimodal Integration Boost the Performance of LLM for Optimization: Case Study on Capacitated Vehicle Routing Problems
Recently, large language models (LLMs) have notably positioned them as capable tools for addressing complex optimization challenges.
MMSR: Symbolic Regression is a Multimodal Task
The SR problem is solved as a pure multimodal problem, and contrastive learning is also introduced in the training process for modal alignment to facilitate later modal feature fusion.
SequentialAttention++ for Block Sparsification: Differentiable Pruning Meets Combinatorial Optimization
Neural network pruning is a key technique towards engineering large yet scalable, interpretable, and generalizable models.
