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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 771780 of 1277 papers

TitleStatusHype
Sparse Multi-Reference Alignment : Phase Retrieval, Uniform Uncertainty Principles and the Beltway Problem0
Matrix Encoding Networks for Neural Combinatorial OptimizationCode1
QUBO transformation using Eigenvalue Decomposition0
BinarizedAttack: Structural Poisoning Attacks to Graph-based Anomaly DetectionCode0
Contingency-Aware Influence Maximization: A Reinforcement Learning ApproachCode1
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
Fair Disaster Containment via Graph-Cut Problems0
Learning Pseudo-Backdoors for Mixed Integer Programs0
A Bi-Level Framework for Learning to Solve Combinatorial Optimization on GraphsCode1
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