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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 821830 of 1277 papers

TitleStatusHype
Learning Geometric Combinatorial Optimization Problems using Self-attention and Domain KnowledgeCode0
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
Sparse Multi-Reference Alignment : Phase Retrieval, Uniform Uncertainty Principles and the Beltway Problem0
QUBO transformation using Eigenvalue Decomposition0
BinarizedAttack: Structural Poisoning Attacks to Graph-based Anomaly DetectionCode0
Solving Graph-based Public Good Games with Tree Search and Imitation LearningCode0
An SMT Based Compositional Algorithm to Solve a Conflict-Free Electric Vehicle Routing Problem0
Learning Pseudo-Backdoors for Mixed Integer Programs0
Fair Disaster Containment via Graph-Cut Problems0
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