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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 11011110 of 1277 papers

TitleStatusHype
Machine Learning Methods for Data Association in Multi-Object Tracking0
Discrepancy-based Evolutionary Diversity Optimization0
Graph2Seq: Scalable Learning Dynamics for Graphs0
Reinforcement Learning for Solving the Vehicle Routing ProblemCode0
When can l_p-norm objective functions be minimized via graph cuts?0
Spatial Field Reconstruction and Sensor Selection in Heterogeneous Sensor Networks with Stochastic Energy Harvesting0
Long Term Memory Network for Combinatorial Optimization Problems0
Evaluation of bioinspired algorithms for the solution of the job scheduling problem0
Maximizing Submodular or Monotone Approximately Submodular Functions by Multi-objective Evolutionary Algorithms0
Scalable Relaxations of Sparse Packing Constraints: Optimal Biocontrol in Predator-Prey Network0
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