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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 11911200 of 1277 papers

TitleStatusHype
On the Runtime of Randomized Local Search and Simple Evolutionary Algorithms for Dynamic Makespan Scheduling0
Tight Bounds on Low-degree Spectral Concentration of Submodular and XOS functions0
Totally Corrective Boosting with Cardinality Penalization0
Feeder Load Balancing using Neural Network0
Importance weighting without importance weights: An efficient algorithm for combinatorial semi-bandits0
Denoising Autoencoders for fast Combinatorial Black Box OptimizationCode1
Inference of hidden structures in complex physical systems by multi-scale clustering0
Faster quantum mixing for slowly evolving sequences of Markov chains0
First-order regret bounds for combinatorial semi-bandits0
Cheaper and Better: Selecting Good Workers for Crowdsourcing0
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