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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 401–450 of 1277 papers

TitleStatusHype
Let the Flows Tell: Solving Graph Combinatorial Problems with GFlowNetsCode1
Implementation of digital MemComputing using standard electronic componentsCode0
Multi-Passive/Active-IRS Enhanced Wireless Coverage: Deployment Optimization and Cost-Performance Trade-off—0
Constraints First: A New MDD-based Model to Generate Sentences Under Constraints—0
User Assignment and Resource Allocation for Hierarchical Federated Learning over Wireless Networks—0
Sub-universal variational circuits for combinatorial optimization problemsCode0
Towards Generalizable Neural Solvers for Vehicle Routing Problems via Ensemble with Transferrable Local PolicyCode1
A Graph Neural Network-Based QUBO-Formulated Hamiltonian-Inspired Loss Function for Combinatorial Optimization using Reinforcement LearningCode0
Multitasking Evolutionary Algorithm Based on Adaptive Seed Transfer for Combinatorial Problem—0
Efficient Joint Optimization of Layer-Adaptive Weight Pruning in Deep Neural NetworksCode1
Accelerating Exact Combinatorial Optimization via RL-based Initialization -- A Case Study in Scheduling—0
Accelerating Diffusion-based Combinatorial Optimization Solvers by Progressive Distillation—0
VN-Solver: Vision-based Neural Solver for Combinatorial Optimization over Graphs—0
Job Shop Scheduling via Deep Reinforcement Learning: a Sequence to Sequence approachCode1
Threshold-aware Learning to Generate Feasible Solutions for Mixed Integer Programs—0
Reinforcement Learning-based Non-Autoregressive Solver for Traveling Salesman ProblemsCode1
Route Planning Using Nature-Inspired Algorithms—0
JoinGym: An Efficient Query Optimization Environment for Reinforcement LearningCode1
A Fast Task Offloading Optimization Framework for IRS-Assisted Multi-Access Edge Computing SystemCode1
Accelerating Cutting-Plane Algorithms via Reinforcement Learning Surrogates—0
Efficiently Factorizing Boolean Matrices using Proximal Gradient Descent—0
Learning to Select and Rank from Choice-Based Feedback: A Simple Nested Approach—0
Transformers in Reinforcement Learning: A Survey—0
FIS-ONE: Floor Identification System with One Label for Crowdsourced RF SignalsCode0
A Graph Multi-separator Problem for Image Segmentation—0
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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