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 4150 of 1277 papers

TitleStatusHype
A Reinforcement Learning Environment For Job-Shop SchedulingCode1
Combinatorial Optimization with Graph Convolutional Networks and Guided Tree SearchCode1
A Deep Instance Generative Framework for MILP Solvers Under Limited Data AvailabilityCode1
A Deep Reinforcement Learning Approach for Solving the Traveling Salesman Problem with DroneCode1
Are Graph Neural Networks Optimal Approximation Algorithms?Code1
Combinatorial Optimization for Panoptic Segmentation: A Fully Differentiable ApproachCode1
Attention, Learn to Solve Routing Problems!Code1
Automatic Truss Design with Reinforcement LearningCode1
Adversarial Immunization for Certifiable Robustness on GraphsCode1
Combinatorial Optimization Perspective based Framework for Multi-behavior RecommendationCode1
Show:102550
← PrevPage 5 of 128Next →

No leaderboard results yet.