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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 11711180 of 1277 papers

TitleStatusHype
Reinforcement Learning for Solving the Vehicle Routing ProblemCode0
DistrictNet: Decision-aware learning for geographical districtingCode0
Optimal Intermittent Particle FilterCode0
Generalization of Machine Learning for Problem Reduction: A Case Study on Travelling Salesman ProblemsCode0
LLMs for Cold-Start Cutting Plane Separator ConfigurationCode0
Understanding Curriculum Learning in Policy Optimization for Online Combinatorial OptimizationCode0
Optimization by Parallel Quasi-Quantum Annealing with Gradient-Based SamplingCode0
Local Energy Distribution Based Hyperparameter Determination for Stochastic Simulated AnnealingCode0
Optimization by Simulated AnnealingCode0
A random-key GRASP for combinatorial optimizationCode0
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