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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 971980 of 1277 papers

TitleStatusHype
Neural Graph Matching Network: Learning Lawler's Quadratic Assignment Problem with Extension to Hypergraph and Multiple-graph Matching0
Neural Large Neighborhood Search0
Neural Lattice Reduction: A Self-Supervised Geometric Deep Learning Approach0
Neural Networks and (Virtual) Extended Formulations0
Neural Networks for Local Search and Crossover in Vehicle Routing: A Possible Overkill?0
Neural Packing: from Visual Sensing to Reinforcement Learning0
Neural Quantum Digital Twins for Optimizing Quantum Annealing0
Neural Topological Ordering for Computation Graphs0
Neuromimetic Linear Systems -- Resilience and Learning0
Neuro-Symbolic Rule Lists0
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