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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 311320 of 1277 papers

TitleStatusHype
Accelerating Exact Combinatorial Optimization via RL-based Initialization -- A Case Study in Scheduling0
A Weighted Common Subgraph Matching Algorithm0
Auxiliary-task Based Deep Reinforcement Learning for Participant Selection Problem in Mobile Crowdsourcing0
An Attention-LSTM Hybrid Model for the Coordinated Routing of Multiple Vehicles0
Automatic Rank Selection for High-Speed Convolutional Neural Network0
An Approximation Algorithm for Risk-averse Submodular Optimization0
Differentiable Greedy Networks0
Differentially Private Partial Set Cover with Applications to Facility Location0
Automatic Loss Function Search for Predict-Then-Optimize Problems with Strong Ranking Property0
Analyzing the behaviour of D'WAVE quantum annealer: fine-tuning parameterization and tests with restrictive Hamiltonian formulations0
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