Combinatorial Optimization
289 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
Large Language Models Can Plan Your Travels Rigorously with Formal Verification Tools
We evaluate our framework with TravelPlanner and achieve a success rate of 97%.
Fewer Truncations Improve Language Modeling
In large language model training, input documents are typically concatenated together and then split into sequences of equal length to avoid padding tokens.
Proposed modified computational model for the amoeba-inspired combinatorial optimization machine
A single-celled amoeba can solve the traveling salesman problem through its shape-changing dynamics.
Graph Reinforcement Learning for Combinatorial Optimization: A Survey and Unifying Perspective
Graphs are a natural representation for systems based on relations between connected entities.
Generative Pre-Trained Transformer for Symbolic Regression Base In-Context Reinforcement Learning
However, its performance is very dependent on the training data and performs poorly on data outside the training set, which leads to poor noise robustness and Versatility of such methods.
Message Passing Variational Autoregressive Network for Solving Intractable Ising Models
Many deep neural networks have been used to solve Ising models, including autoregressive neural networks, convolutional neural networks, recurrent neural networks, and graph neural networks.
Transform then Explore: a Simple and Effective Technique for Exploratory Combinatorial Optimization with Reinforcement Learning
The technique is originated from physics, but is very effective in enabling RL agents to explore to continuously improve the solutions during test.
Age-of-Information-Aware Distributed Task Offloading and Resource Allocation in Mobile Edge Computing Networks
In existing studies, joint optimization of overall task offloading and UA is seldom considered due to the complexity of combinatorial optimization problems, and in cases where it is considered, linear objective functions such as power consumption are adopted.
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.
