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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

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Showing 171–180 of 1277 papers

TitleStatusHype
A Deep Instance Generative Framework for MILP Solvers Under Limited Data AvailabilityCode1
Multi Agent Reinforcement Learning for Sequential Satellite Assignment ProblemsCode1
Large Language Models as Evolutionary OptimizersCode1
Learning Solution-Aware Transformers for Efficiently Solving Quadratic Assignment ProblemCode1
A Deep Reinforcement Learning Algorithm Using Dynamic Attention Model for Vehicle Routing ProblemsCode1
Automatic Truss Design with Reinforcement LearningCode1
Memory-Enhanced Neural Solvers for Efficient Adaptation in Combinatorial OptimizationCode1
Dynamic Partial Removal: A Neural Network Heuristic for Large Neighborhood SearchCode1
A Deep Reinforcement Learning Approach for Solving the Traveling Salesman Problem with DroneCode1
POMO: Policy Optimization with Multiple Optima for Reinforcement LearningCode1
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