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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 241250 of 1277 papers

TitleStatusHype
Assessing Distribution Network Flexibility via Reliability-based P-Q Area Segmentation0
An Iterative Path-Breaking Approach with Mutation and Restart Strategies for the MAX-SAT Problem0
Combinatorial optimization solving by coherent Ising machines based on spiking neural networks0
Combinatorial Optimization via LLM-driven Iterated Fine-tuning0
Characterization of Locality in Spin States and Forced Moves for Optimizations0
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
Combinatorial Persistency Criteria for Multicut and Max-Cut0
CCJA: Context-Coherent Jailbreak Attack for Aligned Large Language Models0
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