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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 491500 of 1277 papers

TitleStatusHype
An Iterative Path-Breaking Approach with Mutation and Restart Strategies for the MAX-SAT Problem0
ERL-MPP: Evolutionary Reinforcement Learning with Multi-head Puzzle Perception for Solving Large-scale Jigsaw Puzzles of Eroded Gaps0
Characterization of Locality in Spin States and Forced Moves for Optimizations0
Fixed Priority Global Scheduling from a Deep Learning Perspective0
Chaos inspired Particle Swarm Optimization with Levy Flight for Genome Sequence Assembly0
An Introduction to Quantum Machine Learning for Engineers0
A Combinatorial Semi-Bandit Approach to Charging Station Selection for Electric Vehicles0
Equivariant neural networks for recovery of Hadamard matrices0
CCJA: Context-Coherent Jailbreak Attack for Aligned Large Language Models0
Entity Summarization: State of the Art and Future Challenges0
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