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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

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Showing 401–450 of 1277 papers

TitleStatusHype
A new dog learns old tricks: RL finds classic optimization algorithms—0
Decomposed Quadratization: Efficient QUBO Formulation for Learning Bayesian Network—0
Efficient correlation-based discretization of continuous variables for annealing machines—0
Boosting Ant Colony Optimization via Solution Prediction and Machine Learning—0
Deep memetic models for combinatorial optimization problems: application to the tool switching problem—0
Efficient LDPC Decoding using Physical Computation—0
Efficient learning by implicit exploration in bandit problems with side observations—0
Efficiently Factorizing Boolean Matrices using Proximal Gradient Descent—0
A Tutorial about Random Neural Networks in Supervised Learning—0
Efficient Optimization Accelerator Framework for Multistate Ising Problems—0
Amplitude-Ensemble Quantum-Inspired Tabu Search Algorithm for Solving 0/1 Knapsack Problems—0
A Data-Driven Column Generation Algorithm For Bin Packing Problem in Manufacturing Industry—0
Efficient Training of Multi-task Combinarotial Neural Solver with Multi-armed Bandits—0
Embed and Project: Discrete Sampling with Universal Hashing—0
End-to-End Efficient Representation Learning via Cascading Combinatorial Optimization—0
Budgeted Influence Maximization for Multiple Products—0
End-to-End Pareto Set Prediction with Graph Neural Networks for Multi-objective Facility Location—0
End-to-end Planning of Fixed Millimeter-Wave Networks—0
Energy Minimization in UAV-Aided Networks: Actor-Critic Learning for Constrained Scheduling Optimization—0
Enhancing Column Generation by Reinforcement Learning-Based Hyper-Heuristic for Vehicle Routing and Scheduling Problems—0
Enhancing GNNs Performance on Combinatorial Optimization by Recurrent Feature Update—0
Enhancing In-vehicle Multiple Object Tracking Systems with Embeddable Ising Machines—0
Enhancing Network Resilience through Machine Learning-powered Graph Combinatorial Optimization: Applications in Cyber Defense and Information Diffusion—0
Enhancing Robustness of Neural Networks through Fourier Stabilization—0
Enhancing variational quantum algorithms by balancing training on classical and quantum hardware—0
Causal Effect Identification in Uncertain Causal Networks—0
Entity Summarization: State of the Art and Future Challenges—0
Causal Discovery with Reinforcement Learning—0
Deep Learning of Graph Matching—0
Equivariant neural networks for recovery of Hadamard matrices—0
Attention Round for Post-Training Quantization—0
Adaptive Non-Uniform Timestep Sampling for Accelerating Diffusion Model Training—0
ERL-MPP: Evolutionary Reinforcement Learning with Multi-head Puzzle Perception for Solving Large-scale Jigsaw Puzzles of Eroded Gaps—0
Deep Learning based Antenna Selection and CSI Extrapolation in Massive MIMO Systems—0
Assessing Distribution Network Flexibility via Reliability-based P-Q Area Segmentation—0
An Iterative Path-Breaking Approach with Mutation and Restart Strategies for the MAX-SAT Problem—0
Estimation of the yield curve for Costa Rica using combinatorial optimization metaheuristics applied to nonlinear regression—0
Estudo comparativo de meta-heurísticas para problemas de colorações de grafos—0
CHARME: A chain-based reinforcement learning approach for the minor embedding problem—0
Evaluating Curriculum Learning Strategies in Neural Combinatorial Optimization—0
Evaluation of bioinspired algorithms for the solution of the job scheduling problem—0
Evolutionary Approach for the Containers Bin-Packing Problem—0
Evolutionary Bi-objective Optimization for the Dynamic Chance-Constrained Knapsack Problem Based on Tail Bound Objectives—0
Evolutionary Construction of Perfectly Balanced Boolean Functions—0
Evolutionary Multi-Objective Algorithms for the Knapsack Problems with Stochastic Profits—0
Evolutionary RL for Container Loading—0
A Meta-heuristically Approach of the Spatial Assignment Problem of Human Resources in Multi-sites Enterprise—0
Fast Hyperparameter Tuning for Ising Machines—0
Exact and Approximate Hierarchical Clustering Using A*—0
Attention-based Reinforcement Learning for Combinatorial Optimization: Application to Job Shop Scheduling Problem—0
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