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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 211220 of 1277 papers

TitleStatusHype
Joint Graph Decomposition and Node Labeling: Problem, Algorithms, ApplicationsCode0
Interferometric Neural NetworksCode0
Intelligent Channel Allocation for IEEE 802.11be Multi-Link Operation: When MAB Meets LLMCode0
Lagrange Oscillatory Neural Networks for Constraint Satisfaction and OptimizationCode0
Improving Optimization Bounds using Machine Learning: Decision Diagrams meet Deep Reinforcement LearningCode0
Implementing a GPU-based parallel MAX-MIN Ant SystemCode0
Implementation of digital MemComputing using standard electronic componentsCode0
Structural Causal Models Reveal Confounder Bias in Linear Program ModellingCode0
Injecting Combinatorial Optimization into MCTS: Application to the Board Game boopCode0
How to Evaluate Machine Learning Approaches for Combinatorial Optimization: Application to the Travelling Salesman ProblemCode0
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