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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 261270 of 1277 papers

TitleStatusHype
Bayesian Optimization of Functions over Node Subsets in GraphsCode0
Randomized heuristic repair for large-scale multidimensional knapsack problem0
Actively Learning Combinatorial Optimization Using a Membership Oracle0
Leader Reward for POMO-Based Neural Combinatorial Optimization0
Prompt Learning for Generalized Vehicle RoutingCode0
Large Neighborhood Prioritized Search for Combinatorial Optimization with Answer Set ProgrammingCode0
Submodular Information Selection for Hypothesis Testing with Misclassification Penalties0
Using Combinatorial Optimization to Design a High quality LLM Solution0
Cons-training Tensor Networks: Embedding and Optimization Over Discrete Linear ConstraintsCode0
Tackling Prevalent Conditions in Unsupervised Combinatorial Optimization: Cardinality, Minimum, Covering, and MoreCode1
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