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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 481490 of 1277 papers

TitleStatusHype
Estimation of the yield curve for Costa Rica using combinatorial optimization metaheuristics applied to nonlinear regression0
Charged particle tracking with quantum annealing-inspired optimization0
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
ERL-MPP: Evolutionary Reinforcement Learning with Multi-head Puzzle Perception for Solving Large-scale Jigsaw Puzzles of Eroded Gaps0
Fewer Truncations Improve Language Modeling0
Characterization of Locality in Spin States and Forced Moves for Optimizations0
Finding Support Examples for In-Context Learning0
Chaos inspired Particle Swarm Optimization with Levy Flight for Genome Sequence Assembly0
An Introduction to Quantum Machine Learning for Engineers0
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