SOTAVerified

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 11211130 of 1277 papers

TitleStatusHype
Sub-universal variational circuits for combinatorial optimization problemsCode0
Decision-Focused Learning to Predict Action Costs for PlanningCode0
Where the Really Hard Quadratic Assignment Problems Are: the QAP-SAT instancesCode0
Pruning Edges and Gradients to Learn Hypergraphs from Larger SetsCode0
Instance-Conditioned Adaptation for Large-scale Generalization of Neural Routing SolverCode0
Injecting Combinatorial Optimization into MCTS: Application to the Board Game boopCode0
Supplementing Recurrent Neural Networks with Annealing to Solve Combinatorial Optimization ProblemsCode0
Budget-Aware Sequential Brick Assembly with Efficient Constraint SatisfactionCode0
Improving Optimization Bounds using Machine Learning: Decision Diagrams meet Deep Reinforcement LearningCode0
Offline Reinforcement Learning for Learning to Dispatch for Job Shop SchedulingCode0
Show:102550
← PrevPage 113 of 128Next →

No leaderboard results yet.