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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 391400 of 1277 papers

TitleStatusHype
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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