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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 10011010 of 1277 papers

TitleStatusHype
A Time-Dependent TSP Formulation for the Design of an Active Debris Removal Mission using Simulated Annealing0
Graph Neural Networks for Maximum Constraint SatisfactionCode1
Global Optimal Path-Based Clustering AlgorithmCode0
Gumbel-softmax Optimization: A Simple General Framework for Combinatorial Optimization Problems on Graphs0
AED: An Anytime Evolutionary DCOP Algorithm0
Exploratory Combinatorial Optimization with Reinforcement LearningCode0
Combining Learned Representations for Combinatorial Optimization0
Story-oriented Image Selection and Placement0
Automated quantum programming via reinforcement learning for combinatorial optimizationCode0
Charged particle tracking with quantum annealing-inspired optimization0
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