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 651660 of 1277 papers

TitleStatusHype
Robust Bayesian Inference for Moving Horizon Estimation0
Budget-Aware Sequential Brick Assembly with Efficient Constraint SatisfactionCode0
Trading off Quality for Efficiency of Community Detection: An Inductive Method across Graphs0
Automatic and effective discovery of quantum kernelsCode0
How Good Is Neural Combinatorial Optimization? A Systematic Evaluation on the Traveling Salesman Problem0
Learning Obstacle-Avoiding Lattice Paths using Swarm Heuristics: Exploring the Bijection to Ordered Trees0
Structured Q-learning For Antibody Design0
Neural Networks for Local Search and Crossover in Vehicle Routing: A Possible Overkill?0
The (Un)Scalability of Heuristic Approximators for NP-Hard Search ProblemsCode0
Cooperative coevolutionary hybrid NSGA-II with Linkage Measurement Minimization for Large-scale Multi-objective optimization0
Show:102550
← PrevPage 66 of 128Next →

No leaderboard results yet.