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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

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Showing 426–450 of 1277 papers

TitleStatusHype
Large Language Models for Supply Chain Optimization—0
Noisy Tensor Ring approximation for computing gradients of Variational Quantum Eigensolver for Combinatorial Optimization—0
Explainable quantum regression algorithm with encoded data structure—0
Learning to Branch in Combinatorial Optimization with Graph Pointer Networks—0
Monte Carlo Policy Gradient Method for Binary OptimizationCode1
A Formal Perspective on Byte-Pair EncodingCode0
RL4CO: an Extensive Reinforcement Learning for Combinatorial Optimization BenchmarkCode4
Automatic Truss Design with Reinforcement LearningCode1
Chance-Constrained Multiple-Choice Knapsack Problem: Model, Algorithms, and ApplicationsCode0
Object Detection based on the Collection of Geometric Evidence—0
D2Match: Leveraging Deep Learning and Degeneracy for Subgraph MatchingCode1
TreeDQN: Learning to minimize Branch-and-Bound treeCode0
An End-to-End Reinforcement Learning Approach for Job-Shop Scheduling Problems Based on Constraint ProgrammingCode1
Minimizing Energy Consumption in MU-MIMO via Antenna Muting by Neural Networks with Asymmetric Loss—0
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
Meta-SAGE: Scale Meta-Learning Scheduled Adaptation with Guided Exploration for Mitigating Scale Shift on Combinatorial OptimizationCode1
Barriers for the performance of graph neural networks (GNN) in discrete random structures. A comment on~schuetz2022combinatorial,angelini2023modern,schuetz2023reply—0
Discovering Dynamic Causal Space for DAG Structure LearningCode1
Symmetric Replay Training: Enhancing Sample Efficiency in Deep Reinforcement Learning for Combinatorial OptimizationCode0
Dynamic Algorithms for Matroid Submodular Maximization—0
Towards Omni-generalizable Neural Methods for Vehicle Routing ProblemsCode1
DHRL-FNMR: An Intelligent Multicast Routing Approach Based on Deep Hierarchical Reinforcement Learning in SDNCode1
Clustering Method for Time-Series Images Using Quantum-Inspired Computing Technology—0
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