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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 531540 of 1277 papers

TitleStatusHype
Differentiable Model Selection for Ensemble LearningCode0
Binary sequence set optimization for CDMA applications via mixed-integer quadratic programming0
Artificial Potential Field-Based Path Planning for Cluttered EnvironmentsCode0
Learning Heuristics for the Maximum Clique Enumeration Problem Using Low Dimensional Representations0
End-to-End Pareto Set Prediction with Graph Neural Networks for Multi-objective Facility Location0
Learning Discrete Directed Acyclic Graphs via Backpropagation0
Revealed Preferences of One-Sided Matching0
Sub-network Multi-objective Evolutionary Algorithm for Filter Pruning0
SurCo: Learning Linear Surrogates For Combinatorial Nonlinear Optimization Problems0
NeuroPrim: An Attention-based Model for Solving NP-hard Spanning Tree ProblemsCode0
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