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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 826–850 of 1277 papers

TitleStatusHype
Learning to Make Decisions via Submodular Regularization—0
Learning to Solve Multi-Robot Task Allocation with a Covariant-Attention based Neural Architecture—0
ScheduleNet: Learn to Solve MinMax mTSP Using Reinforcement Learning with Delayed Reward—0
Combinatorial Pure Exploration with Full-bandit Feedback and Beyond: Solving Combinatorial Optimization under Uncertainty with Limited Observation—0
Solving the Travelling Thief Problem based on Item Selection Weight and Reverse Order Allocation—0
Revocable Deep Reinforcement Learning with Affinity Regularization for Outlier-Robust Graph MatchingCode2
Neural Knapsack: A Neural Network Based Solver for the Knapsack ProblemCode0
Combining Reinforcement Learning with Lin-Kernighan-Helsgaun Algorithm for the Traveling Salesman ProblemCode1
Fixed Priority Global Scheduling from a Deep Learning Perspective—0
Divide and Learn: A Divide and Conquer Approach for Predict+Optimize—0
Content Provider Dynamics and Coordination in Recommendation Ecosystems—0
Dynamic Submodular Maximization—0
Hybrid Genetic Search for the CVRP: Open-Source Implementation and SWAP* NeighborhoodCode1
CLIPPER: A Graph-Theoretic Framework for Robust Data AssociationCode1
Distributed Injection-Locking in Analog Ising Machines to Solve Combinatorial Optimizations—0
Modular Multi Target Tracking Using LSTM Networks—0
A Knowledge Representation Approach to Automated Mathematical Modelling—0
Evaluating Curriculum Learning Strategies in Neural Combinatorial Optimization—0
Ecole: A Gym-like Library for Machine Learning in Combinatorial Optimization SolversCode1
A Review of the Family of Artificial Fish Swarm Algorithms: Recent Advances and Applications—0
Contrastive Losses and Solution Caching for Predict-and-OptimizeCode0
Geometric Deep Reinforcement Learning for Dynamic DAG SchedulingCode1
A Reinforcement Learning Approach to the Orienteering Problem with Time WindowsCode1
Storage placement policy for minimizing frequency deviation: A combinatorial optimization approach—0
A Spectral Method for Unsupervised Multi-Document Summarization—0
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