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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 11811190 of 1277 papers

TitleStatusHype
Too Big, so Fail? -- Enabling Neural Construction Methods to Solve Large-Scale Routing ProblemsCode0
Futureproof Static Memory PlanningCode0
Smart Predict-and-Optimize for Hard Combinatorial Optimization ProblemsCode0
Coupled Input-Output Dimension Reduction: Application to Goal-oriented Bayesian Experimental Design and Global Sensitivity AnalysisCode0
Artificial Potential Field-Based Path Planning for Cluttered EnvironmentsCode0
Synthesizing Interpretable Control Policies through Large Language Model Guided SearchCode0
A Graph Neural Network-Based QUBO-Formulated Hamiltonian-Inspired Loss Function for Combinatorial Optimization using Reinforcement LearningCode0
OsmLocator: locating overlapping scatter marks with a non-training generative perspectiveCode0
Training Greedy Policy for Proposal Batch Selection in Expensive Multi-Objective Combinatorial OptimizationCode0
Approximation Algorithms for Combinatorial Optimization with PredictionsCode0
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