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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 921930 of 1277 papers

TitleStatusHype
Matroid Bandits: Fast Combinatorial Optimization with Learning0
Maximizing Non-Monotone DR-Submodular Functions with Cardinality Constraints0
Maximizing Submodular or Monotone Approximately Submodular Functions by Multi-objective Evolutionary Algorithms0
Max-Product Belief Propagation for Linear Programming: Applications to Combinatorial Optimization0
MBL-CPDP: A Multi-objective Bilevel Method for Cross-Project Defect Prediction via Automated Machine Learning0
MC-CIM: Compute-in-Memory with Monte-Carlo Dropouts for Bayesian Edge Intelligence0
Melding the Data-Decisions Pipeline: Decision-Focused Learning for Combinatorial Optimization0
Memcomputing: Leveraging memory and physics to compute efficiently0
Message Passing and Combinatorial Optimization0
Message Passing Variational Autoregressive Network for Solving Intractable Ising Models0
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