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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 561570 of 1277 papers

TitleStatusHype
Neural Networks for Local Search and Crossover in Vehicle Routing: A Possible Overkill?0
The (Un)Scalability of Heuristic Approximators for NP-Hard Search ProblemsCode0
Cooperative coevolutionary hybrid NSGA-II with Linkage Measurement Minimization for Large-scale Multi-objective optimization0
A Nested Genetic Algorithm for Explaining Classification Data Sets with Decision Rules0
One Model, Any CSP: Graph Neural Networks as Fast Global Search Heuristics for Constraint SatisfactionCode0
Combinatorial optimization solving by coherent Ising machines based on spiking neural networks0
Evaluate Quantum Combinatorial Optimization for Distribution Network ReconfigurationCode0
Combining Gradients and Probabilities for Heterogeneous Approximation of Neural NetworksCode0
Causal Effect Identification in Uncertain Causal Networks0
Neural Set Function Extensions: Learning with Discrete Functions in High DimensionsCode0
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