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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 726–750 of 1277 papers

TitleStatusHype
Route Planning Using Nature-Inspired Algorithms—0
Routing Arena: A Benchmark Suite for Neural Routing Solvers—0
Runtime Analysis of Evolutionary Algorithms with Biased Mutation for the Multi-Objective Minimum Spanning Tree Problem—0
Runtime Performances of Randomized Search Heuristics for the Dynamic Weighted Vertex Cover Problem—0
Safe Element Screening for Submodular Function Minimization—0
Sample Complexity of Automated Mechanism Design—0
Sample Selection for Fair and Robust Training—0
SatNet: A Benchmark for Satellite Scheduling Optimization—0
Scalability of using Restricted Boltzmann Machines for Combinatorial Optimization—0
Learning NP-Hard Multi-Agent Assignment Planning using GNN: Inference on a Random Graph and Provable Auction-Fitted Q-learning—0
Scalable Anomaly Detection in Large Homogenous Populations—0
Scalable Discrete Diffusion Samplers: Combinatorial Optimization and Statistical Physics—0
Scalable Feature Subset Selection for Big Data using Parallel Hybrid Evolutionary Algorithm based Wrapper in Apache Spark—0
Scalable iterative pruning of large language and vision models using block coordinate descent—0
Scalable Quantum-Inspired Optimization through Dynamic Qubit Compression—0
Scalable Relaxations of Sparse Packing Constraints: Optimal Biocontrol in Predator-Prey Network—0
Scaling Combinatorial Optimization Neural Improvement Heuristics with Online Search and Adaptation—0
ScheduleNet: Learn to Solve MinMax mTSP Using Reinforcement Learning with Delayed Reward—0
Searching Large Neighborhoods for Integer Linear Programs with Contrastive Learning—0
Second Order Swarm Intelligence—0
Security Defense of Large Scale Networks Under False Data Injection Attacks: An Attack Detection Scheduling Approach—0
Segmentation and Optimal Region Selection of Physiological Signals using Deep Neural Networks and Combinatorial Optimization—0
Selection of Filters for Photonic Crystal Spectrometer Using Domain-Aware Evolutionary Algorithms—0
Self-Assignment Flows for Unsupervised Data Labeling on Graphs—0
Self-Evaluation for Job-Shop Scheduling—0
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