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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 631640 of 1277 papers

TitleStatusHype
Data-driven Prediction of Relevant Scenarios for Robust Combinatorial Optimization0
A Distribution Evolutionary Algorithm for the Graph Coloring Problem0
Pareto Set Learning for Neural Multi-objective Combinatorial OptimizationCode1
MolGenSurvey: A Systematic Survey in Machine Learning Models for Molecule Design0
Focused Jump-and-Repair Constraint Handling for Fixed-Parameter Tractable Graph Problems Closed Under Induced Subgraphs0
Optimizing Camera Placements for Overlapped Coverage with 3D Camera Projections0
A Simple and Computationally Trivial Estimator for Grouped Fixed Effects Models0
A Differentiable Approach to Combinatorial Optimization using Dataless Neural Networks0
A Compositional Algorithm for the Conflict-Free Electric Vehicle Routing Problem0
Set-valued prediction in hierarchical classification with constrained representation complexity0
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