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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 761770 of 1277 papers

TitleStatusHype
MODRL/D-EL: Multiobjective Deep Reinforcement Learning with Evolutionary Learning for Multiobjective Optimization0
An Overview and Experimental Study of Learning-based Optimization Algorithms for Vehicle Routing Problem0
USCO-Solver: Solving Undetermined Stochastic Combinatorial Optimization ProblemsCode0
A Fitness Landscape View on the Tuning of an Asynchronous Master-Worker EA for Nuclear Reactor Design0
Learning Geometric Combinatorial Optimization Problems using Self-attention and Domain KnowledgeCode0
Maximum Entropy Weighted Independent Set Pooling for Graph Neural NetworksCode1
Learning Primal Heuristics for Mixed Integer ProgramsCode1
Combinatorial Optimization with Physics-Inspired Graph Neural NetworksCode1
Pruning Edges and Gradients to Learn Hypergraphs from Larger SetsCode0
Scalable Feature Subset Selection for Big Data using Parallel Hybrid Evolutionary Algorithm based Wrapper in Apache Spark0
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