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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 941950 of 1277 papers

TitleStatusHype
A Multi-task Selected Learning Approach for Solving 3D Flexible Bin Packing Problem0
Analysis of Quality Diversity Algorithms for the Knapsack Problem0
Analyzing the behaviour of D'WAVE quantum annealer: fine-tuning parameterization and tests with restrictive Hamiltonian formulations0
An Approximation Algorithm for Risk-averse Submodular Optimization0
An Attention-LSTM Hybrid Model for the Coordinated Routing of Multiple Vehicles0
An Edge-Aware Graph Autoencoder Trained on Scale-Imbalanced Data for Traveling Salesman Problems0
An Efficient Algorithm for Cooperative Semi-Bandits0
An efficient algorithm for learning with semi-bandit feedback0
An Efficient Circuit Compilation Flow for Quantum Approximate Optimization Algorithm0
An Efficient Learning-based Solver Comparable to Metaheuristics for the Capacitated Arc Routing Problem0
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