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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 841850 of 1277 papers

TitleStatusHype
Towards the Inferrence of Structural Similarity of Combinatorial Landscapes0
Trading off Quality for Efficiency of Community Detection: An Inductive Method across Graphs0
Trading Quality for Efficiency of Graph Partitioning: An Inductive Method across Graphs0
Training Deep Boltzmann Networks with Sparse Ising Machines0
Training Hard-Threshold Networks with Combinatorial Search in a Discrete Target Propagation Setting0
Transfer Learning for Deep-Unfolded Combinatorial Optimization Solver with Quantum Annealer0
Transformers in Reinforcement Learning: A Survey0
Transform then Explore: a Simple and Effective Technique for Exploratory Combinatorial Optimization with Reinforcement Learning0
Tropical Attention: Neural Algorithmic Reasoning for Combinatorial Algorithms0
TSS GAZ PTP: Towards Improving Gumbel AlphaZero with Two-stage Self-play for Multi-constrained Electric Vehicle Routing Problems0
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