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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
Contingency-Aware Influence Maximization: A Reinforcement Learning ApproachCode1
Modern graph neural networks do worse than classical greedy algorithms in solving combinatorial optimization problems like maximum independent setCode1
A Word is Worth A Thousand Dollars: Adversarial Attack on Tweets Fools Stock PredictionCode1
DeepACO: Neural-enhanced Ant Systems for Combinatorial OptimizationCode1
A Deep Instance Generative Framework for MILP Solvers Under Limited Data AvailabilityCode1
Deep Graph Matching via Blackbox Differentiation of Combinatorial SolversCode1
Attention, Learn to Solve Routing Problems!Code1
DHRL-FNMR: An Intelligent Multicast Routing Approach Based on Deep Hierarchical Reinforcement Learning in SDNCode1
ASP: Learn a Universal Neural Solver!Code1
A Two-stage Reinforcement Learning-based Approach for Multi-entity Task AllocationCode1
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