Combinatorial Optimization
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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.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/akimoto-cris/rd_vit_prune
공식 코드Efficient Joint Optimization of Layer-Adaptive Weight Pruning in Deep Neural Networks
https://github.com/alexgraikos/diffusion_priors
공식 코드https://github.com/aliaksah/depmixS4pp
공식 코드An adaptive simulated annealing EM algorithm for inference on non-homogeneous hidden Markov models
https://github.com/alstn12088/sym-nco
공식 코드Sym-NCO: Leveraging Symmetricity for Neural Combinatorial Optimization
https://github.com/amaurygouverneur/optimal_measurement_times_for_particle_filtering
공식 코드https://github.com/amazon-research/co-with-gnns-example
공식 코드Combinatorial Optimization with Physics-Inspired Graph Neural Networks
https://github.com/amazon-science/co-with-gnns-example
공식 코드Combinatorial Optimization with Automated Graph Neural Networks
https://github.com/amf272/Distributional-MIPLIB-Generators
공식 코드Distributional MIPLIB: a Multi-Domain Library for Advancing ML-Guided MILP Methods
https://github.com/anhphuong-ngo/Distribution_Network_Reconfiguration_Model_on_Quantum_Computer
공식 코드Evaluate Quantum Combinatorial Optimization for Distribution Network Reconfiguration
https://github.com/ankurnath/maxcut-bench
공식 코드https://github.com/antoniochaves19/rk-grasp
공식 코드https://github.com/artemyk/pid-as-ib
공식 코드Partial information decomposition: redundancy as information bottleneck
https://github.com/arunsehrawat/interferometric-neural-networks
공식 코드https://github.com/askolik/eqc_for_nco
공식 코드Equivariant quantum circuits for learning on weighted graphs
https://github.com/attila-lischka/GREAT
공식 코드https://github.com/awakermhy/heo
공식 코드https://github.com/axelparmentier/MinimumWeightTwoStageSpanningTree.jl
공식 코드Learning with Combinatorial Optimization Layers: a Probabilistic Approach
https://github.com/axelparmentier/inferopt.jl
공식 코드Learning with Combinatorial Optimization Layers: a Probabilistic Approach
https://github.com/banbaralab/kr2024
공식 코드Large Neighborhood Prioritized Search for Combinatorial Optimization with Answer Set Programming
https://github.com/batyleo/stochasticvehiclescheduling.jl
공식 코드Learning with Combinatorial Optimization Layers: a Probabilistic Approach
https://github.com/bill-cjb/nhde
공식 코드Neural Multi-Objective Combinatorial Optimization with Diversity Enhancement
https://github.com/bjoern-andres/graph
공식 코드Joint Graph Decomposition and Node Labeling: Problem, Algorithms, Applications
https://github.com/bokveizen/uco_derand
공식 코드https://github.com/bokveizen/unsupervised-CO-ucom2
공식 코드https://github.com/brendenpetersen/deep-symbolic-optimization
공식 코드Symbolic Regression via Neural-Guided Genetic Programming Population Seeding
https://github.com/brunompacheco/sat-gnn
공식 코드Graph Neural Networks for the Offline Nanosatellite Task Scheduling Problem
https://github.com/caidog1129/milp_multitask
공식 코드Multi-task Representation Learning for Mixed Integer Linear Programming
https://github.com/caigaojiang/llmopt
공식 코드LLMOPT: Learning to Define and Solve General Optimization Problems from Scratch
https://github.com/cambridgeltl/claps
공식 코드https://github.com/cappadokes/idealloc
공식 코드https://github.com/carlossshi/neuroprim
공식 코드NeuroPrim: An Attention-based Model for Solving NP-hard Spanning Tree Problems
https://github.com/carrasqu/binncode
공식 코드Quantum HyperNetworks: Training Binary Neural Networks in Quantum Superposition
https://github.com/ccqym/osmlocator
공식 코드OsmLocator: locating overlapping scatter marks with a non-training generative perspective
https://github.com/chaitjo/learning-tsp
공식 코드Learning the Travelling Salesperson Problem Requires Rethinking Generalization
https://github.com/chakrabarti/pairwise_constrained_clustering
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.