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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 401–425 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
A Data-Driven Column Generation Algorithm For Bin Packing Problem in Manufacturing Industry—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
Deep Learning of Graph Matching—0
Efficient Optimization Accelerator Framework for Multistate Ising Problems—0
Attention Round for Post-Training Quantization—0
Adaptive Non-Uniform Timestep Sampling for Accelerating Diffusion Model Training—0
Efficient Training of Multi-task Combinarotial Neural Solver with Multi-armed Bandits—0
Embed and Project: Discrete Sampling with Universal Hashing—0
Deep Learning based Antenna Selection and CSI Extrapolation in Massive MIMO Systems—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
A Meta-heuristically Approach of the Spatial Assignment Problem of Human Resources in Multi-sites Enterprise—0
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