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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 911920 of 1277 papers

TitleStatusHype
Revisiting Robust Model Fitting Using Truncated LossCode0
Bayesian preference elicitation for multiobjective combinatorial optimization0
Boosting Ant Colony Optimization via Solution Prediction and Machine Learning0
Performance Analysis of Meta-heuristic Algorithms for a Quadratic Assignment Problem0
Positive Semidefinite Matrix Factorization: A Connection with Phase Retrieval and Affine Rank MinimizationCode0
DeepCO: Offline Combinatorial Optimization Framework Utilizing Deep Learning0
A Generative Graph Method to Solve the Travelling Salesman Problem0
Curriculum learning for multilevel budgeted combinatorial problemsCode0
Learning Combined Set Covering and Traveling Salesman Problem0
A Survey on Recent Progress in the Theory of Evolutionary Algorithms for Discrete Optimization0
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