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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

Showing 341350 of 1277 papers

TitleStatusHype
Evolution of Heuristics: Towards Efficient Automatic Algorithm Design Using Large Language ModelCode3
Quantum-Hybrid Stereo Matching With Nonlinear Regularization and Spatial Pyramids0
OsmLocator: locating overlapping scatter marks with a non-training generative perspectiveCode0
NN-Steiner: A Mixed Neural-algorithmic Approach for the Rectilinear Steiner Minimum Tree Problem0
A Unified Pre-training and Adaptation Framework for Combinatorial Optimization on Graphs0
COMBHelper: A Neural Approach to Reduce Search Space for Graph Combinatorial ProblemsCode0
Symmetry Breaking and Equivariant Neural Networks0
A Simulated Annealing-Based Multiobjective Optimization Algorithm for Minimum Weight Minimum Connected Dominating Set Problem0
Machine Learning for the Multi-Dimensional Bin Packing Problem: Literature Review and Empirical Evaluation0
A Novel Differentiable Loss Function for Unsupervised Graph Neural Networks in Graph Partitioning0
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