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Combinatorial Optimization

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.

Source: Recent Advances in Neural Program Synthesis

Papers

Showing 101125 of 1277 papers

TitleStatusHype
Modern graph neural networks do worse than classical greedy algorithms in solving combinatorial optimization problems like maximum independent setCode1
Equivariant quantum circuits for learning on weighted graphsCode1
A Reinforcement Learning Environment For Job-Shop SchedulingCode1
DAG Matters! GFlowNets Enhanced Explainer For Graph Neural NetworksCode1
A Reinforcement Learning Approach to the Orienteering Problem with Time WindowsCode1
DataSculpt: Crafting Data Landscapes for Long-Context LLMs through Multi-Objective PartitioningCode1
A Fast Task Offloading Optimization Framework for IRS-Assisted Multi-Access Edge Computing SystemCode1
DeepACO: Neural-enhanced Ant Systems for Combinatorial OptimizationCode1
Deep Graph Matching via Blackbox Differentiation of Combinatorial SolversCode1
Deep Graph Matching via Blackbox Differentiation of Combinatorial SolversCode1
Denoising Autoencoders for fast Combinatorial Black Box OptimizationCode1
DHRL-FNMR: An Intelligent Multicast Routing Approach Based on Deep Hierarchical Reinforcement Learning in SDNCode1
Let the Flows Tell: Solving Graph Combinatorial Problems with GFlowNetsCode1
ASP: Learn a Universal Neural Solver!Code1
Erdos Goes Neural: an Unsupervised Learning Framework for Combinatorial Optimization on GraphsCode1
Matrix Encoding Networks for Neural Combinatorial OptimizationCode1
Combining Reinforcement Learning with Lin-Kernighan-Helsgaun Algorithm for the Traveling Salesman ProblemCode1
DIMES: A Differentiable Meta Solver for Combinatorial Optimization ProblemsCode1
A Bi-Level Framework for Learning to Solve Combinatorial Optimization on GraphsCode1
A Cooperative Multi-Agent Reinforcement Learning Framework for Resource Balancing in Complex Logistics NetworkCode1
DOGE-Train: Discrete Optimization on GPU with End-to-end TrainingCode1
Ecole: A Gym-like Library for Machine Learning in Combinatorial Optimization SolversCode1
A Comprehensive Evaluation of Contemporary ML-Based Solvers for Combinatorial OptimizationCode1
Efficient Joint Optimization of Layer-Adaptive Weight Pruning in Deep Neural NetworksCode1
Are Graph Neural Networks Optimal Approximation Algorithms?Code1
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