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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 691700 of 1277 papers

TitleStatusHype
Minimizing Polarization and Disagreement in Social Networks via Link Recommendation0
Reinforcement Learning Enhanced Explainer for Graph Neural Networks0
Eliciting and Distinguishing Between Weak and Incomplete Preferences: Theory, Experiment and Computation0
Nonequilibrium Monte Carlo for unfreezing variables in hard combinatorial optimization0
SatNet: A Benchmark for Satellite Scheduling Optimization0
Learning to Schedule Heuristics for the Simultaneous Stochastic Optimization of Mining Complexes0
Reversible Action Design for Combinatorial Optimization with ReinforcementLearning0
Asteroid Flyby Cycler Trajectory Design Using Deep Neural Networks0
BiGrad: Differentiating through Bilevel Optimization Programming0
Active Learning Meets Optimized Item SelectionCode1
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