Steiner Tree Problem
7 papers with code • 0 benchmarks • 1 datasets
The Steiner tree problem is a computational problem in computer science and graph theory that involves finding the minimum weight subgraph in an undirected graph that connects a given set of terminal vertices. The goal of the Steiner tree problem is to minimize the total weight of the edges in the subgraph, and it is considered NP-hard, meaning that finding the optimal solution is computationally difficult.
Benchmarks
These leaderboards are used to track progress in Steiner Tree Problem
PACE 2018 Steiner Tree
Most implemented papers
DynASP2.5: Dynamic Programming on Tree Decompositions in Action
In this paper, we describe underlying concepts of our new implementation (DynASP2. 5) that shows competitive behavior to state-of-the-art ASP solvers even for finding just one solution when solving problems as the Steiner tree problem that have been modeled in ASP on graphs with low treewidth.
Solving the Steiner Tree Problem with few Terminals
We show that admissibility is indeed weaker than consistency and establish correctness of the DS* algorithm when using an admissible heuristic function.
The Power of Many: A Physarum Swarm Steiner Tree Algorithm
We create a novel Physarum Steiner algorithm designed to solve the Euclidean Steiner tree problem.
Learning-Augmented Algorithms for Online Steiner Tree
Steiner tree is known to have strong lower bounds in the online setting and any algorithm's worst-case guarantee is far from desirable.
Deep-Steiner: Learning to Solve the Euclidean Steiner Tree Problem
The Euclidean Steiner tree problem seeks the min-cost network to connect a collection of target locations, and it underlies many applications of wireless networks.
Query-decision Regression between Shortest Path and Minimum Steiner Tree
Considering a graph with unknown weights, can we find the shortest path for a pair of nodes if we know the minimal Steiner trees associated with some subset of nodes?
Approximation Algorithms for Combinatorial Optimization with Predictions
With small enough prediction error we achieve approximation guarantees that are beyond reach without predictions in the given time bounds, as exemplified by the NP-hardness and APX-hardness of many of the above problems.
