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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 8190 of 1277 papers

TitleStatusHype
Balans: Multi-Armed Bandits-based Adaptive Large Neighborhood Search for Mixed-Integer Programming ProblemCode1
Belief Propagation Neural NetworksCode1
Dynamic Partial Removal: A Neural Network Heuristic for Large Neighborhood SearchCode1
Ecole: A Gym-like Library for Machine Learning in Combinatorial Optimization SolversCode1
DataSculpt: Crafting Data Landscapes for Long-Context LLMs through Multi-Objective PartitioningCode1
Efficient Joint Optimization of Layer-Adaptive Weight Pruning in Deep Neural NetworksCode1
A Deep Reinforcement Learning Algorithm Using Dynamic Attention Model for Vehicle Routing ProblemsCode1
Quantum approximate optimization via learning-based adaptive optimizationCode1
A Deep Reinforcement Learning Approach for Solving the Traveling Salesman Problem with DroneCode1
DeepACO: Neural-enhanced Ant Systems for Combinatorial OptimizationCode1
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