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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 376400 of 1277 papers

TitleStatusHype
Interferometric Neural NetworksCode0
Neural Multi-Objective Combinatorial Optimization with Diversity EnhancementCode1
Survival of the Most Influential Prompts: Efficient Black-Box Prompt Search via Clustering and PruningCode1
Neural Packing: from Visual Sensing to Reinforcement Learning0
Exploring the Power of Graph Neural Networks in Solving Linear Optimization ProblemsCode1
On permutation symmetries in Bayesian neural network posteriors: a variational perspective0
Enhancing Column Generation by Reinforcement Learning-Based Hyper-Heuristic for Vehicle Routing and Scheduling Problems0
Neural Combinatorial Optimization with Heavy Decoder: Toward Large Scale GeneralizationCode1
Graph-SCP: Accelerating Set Cover Problems with Graph Neural NetworksCode0
GRASP: Accelerating Shortest Path Attacks via Graph Attention0
Diversity from Human Feedback0
An Edge-Aware Graph Autoencoder Trained on Scale-Imbalanced Data for Traveling Salesman Problems0
Oracle Efficient Algorithms for Groupwise Regret0
Routing Arena: A Benchmark Suite for Neural Routing Solvers0
A Deep Instance Generative Framework for MILP Solvers Under Limited Data AvailabilityCode1
GenCO: Generating Diverse Designs with Combinatorial Constraints0
Are Graph Neural Networks Optimal Approximation Algorithms?Code1
Too Big, so Fail? -- Enabling Neural Construction Methods to Solve Large-Scale Routing ProblemsCode0
Controlling Continuous Relaxation for Combinatorial OptimizationCode0
Genetic Engineering Algorithm (GEA): An Efficient Metaheuristic Algorithm for Solving Combinatorial Optimization Problems0
DeepACO: Neural-enhanced Ant Systems for Combinatorial OptimizationCode1
Enhancing Network Resilience through Machine Learning-powered Graph Combinatorial Optimization: Applications in Cyber Defense and Information Diffusion0
QAL-BP: An Augmented Lagrangian Quantum Approach for Bin PackingCode0
Efficient LDPC Decoding using Physical Computation0
Let the Flows Tell: Solving Graph Combinatorial Problems with GFlowNetsCode1
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