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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 1151–1175 of 1277 papers

TitleStatusHype
Dynamic Submodular Maximization—0
EALG: Evolutionary Adversarial Generation of Language Model-Guided Generators for Combinatorial Optimization—0
Early years of Biased Random-Key Genetic Algorithms: A systematic review—0
EB-GLS: An Improved Guided Local Search Based on the Big Valley Structure—0
Effective anytime algorithm for multiobjective combinatorial optimization problems—0
Effective Features of Remote Sensing Image Classification Using Interactive Adaptive Thresholding Method—0
Efficient 3D Endfiring TRUS Prostate Segmentation with Globally Optimized Rotational Symmetry—0
Efficient Algorithms for Adversarial Contextual Learning—0
Efficient Combinatorial Optimization for Word-level Adversarial Textual Attack—0
Decomposed Quadratization: Efficient QUBO Formulation for Learning Bayesian Network—0
Efficient correlation-based discretization of continuous variables for annealing machines—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
Efficient Optimization Accelerator Framework for Multistate Ising Problems—0
Efficient Optimization with Higher-Order Ising Machines—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
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
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