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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 561570 of 1277 papers

TitleStatusHype
Dynamic Programming on a Quantum Annealer: Solving the RBC ModelCode0
Policy-Based Self-Competition for Planning ProblemsCode0
Learning-Based Heuristic for Combinatorial Optimization of the Minimum Dominating Set Problem using Graph Convolutional NetworksCode0
Barriers for the performance of graph neural networks (GNN) in discrete random structures. A comment on~schuetz2022combinatorial,angelini2023modern,schuetz2023reply0
Symmetric Replay Training: Enhancing Sample Efficiency in Deep Reinforcement Learning for Combinatorial OptimizationCode0
Dynamic Algorithms for Matroid Submodular Maximization0
Clustering Method for Time-Series Images Using Quantum-Inspired Computing Technology0
The First Proven Performance Guarantees for the Non-Dominated Sorting Genetic Algorithm II (NSGA-II) on a Combinatorial Optimization Problem0
Neural Bee Colony Optimization: A Case Study in Public Transit Network Design0
Efficient Training of Multi-task Combinarotial Neural Solver with Multi-armed Bandits0
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