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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 1176–1200 of 1277 papers

TitleStatusHype
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
Entity Summarization: State of the Art and Future Challenges—0
Equivariant neural networks for recovery of Hadamard matrices—0
ERL-MPP: Evolutionary Reinforcement Learning with Multi-head Puzzle Perception for Solving Large-scale Jigsaw Puzzles of Eroded Gaps—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
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
Evolution as a Service: A Privacy-Preserving Genetic Algorithm for Combinatorial Optimization—0
Evolving Hard Maximum Cut Instances for Quantum Approximate Optimization Algorithms—0
Exact and Approximate Hierarchical Clustering Using A*—0
Exact and heuristic methods for the discrete parallel machine scheduling location problem—0
Exact Combinatorial Optimization with Temporo-Attentional Graph Neural Networks—0
Exact Spin Elimination in Ising Hamiltonians and Energy-Based Machine Learning—0
Expediting Distributed DNN Training with Device Topology-Aware Graph Deployment—0
Experimental Analysis of Design Elements of Scalarizing Functions-based Multiobjective Evolutionary Algorithms—0
Experiments with graph convolutional networks for solving the vertex p-center problem—0
Exploiting Problem Structure in Combinatorial Landscapes: A Case Study on Pure Mathematics Application—0
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