Metric Steiner Tree

Sat 08 December 2018 / In categories approximation, graph theory

2-approximation, euler cycle, hamiltonian path, measure theory, steiner tree

The Metric Steiner Tree


A Steiner Tree is an optimization algorithm for finding a subtree spanning so called terminal vertices. Terminal vertices are the nodes in a network which must spanned by the MST. This problems arise in railway networks, telecommunication networks and VSLI chip design.

This article describes an 2-approximation algorithm for the Metric Steiner Tree Problem. Note that 2-approximation guarantees a solution $2 \cdot OPT$ on worst case situations. If you look for the best approximation, the best known algorithm is a $ 9695 $-approximation.


The Steiner Tree Problem (STP) is a graph $G = (V,E)$. $V$ is splitted into two sets $R$ of required terminal vertices and $S$ of optional Steiner vertices. A subgraph of $G$ is a feasible solution if it spans all vertices of $R$. The objective is to minimize the cost.

The Metric Steiner Tree Problem (MSTP) is a specialization of the vanilla Steiner Tree $X = R \cup S$ along with a non-negative distance function $d : X \times X \mapsto \mathbb{R}_{\geq 0}$ for edges. A metric have two properties: $$ \begin{align} \forall x,y \in X &: d(x,y) = d(y,x) \nonumber \newline \forall x,y,z \in X &: d(x,y) \leq d(x,z) + d(z,y) \nonumber \end{align} $$

This gives an undirected graph $G = (V,E)$ with non-negative edge costs.

Problem: Find a minimal cost tree $T$, which contains all terminal vertives, and possibly some of the optional points, such that the cost $C$ with metric $d$ $$ \begin{align} C_d (T) := \sum_{(u,v) \in E} d(u, v) \nonumber \end{align} $$ of the tree is minimized.

Non-optimal spanning trees

Let $G$ be a $K_4$ graph.

Find a Minimal Spanning Tree (MST) with Kruskal on the terminal vertices. The algorithm come up with this solution:

But this is not optimal! The STP would generate a better MST for that example, since ST can also consider the optional Steiner vertices for calculation. Next we look for a simple and easy to implement approximation for the STP.

Towards an approximation

In this section we show that the approximation bound for MSTP is not greater than for STP.

Define $G’ = (V, E’$) from $G$ as follows: $G’$ is a complete graph and $e’_{ij}$ is the length of the shortets path in $G$ for vertices $i$ to $j$.

The Steiner Tree algorithm would came up with this graph $G’$

The paths in $G’$ cannot be longer than $G$, therefore $$ \begin{align} OPT(G’) \leq OPT(G) \end{align} $$

Note that the graph is still connected.

Now we assume to have a heuristic $H$ for $G’$. We get a solution for $G$ by replacing each edge of the MST in $G$ with its corresponding shortest path in $G’$. Resolve cycles by removing appropriate edges. $$ \begin{align} C^H(G) \leq C^H(G’) \end{align} $$ and therefore by (1) and (2) $$ \begin{align} \frac{C^H(G’)}{OPT(G’)} \leq \alpha  \implies  \frac{C^H(G)}{OPT(G)} \leq \alpha \end{align} $$

Thus, each approximation algorithm for the general STP with bound $\alpha$ gives an algorithm with bound $\alpha$ for the specific MSTP. Why the bound is 2 is illustrated in the next section.

An approximation algorithm

Consider a Steiner Tree with optimal cost $OPT$ for $G’$.

Find a MST on the termial vertices.

To ensure an even degree of all vertices, we double each edge. A graph with even degree have an eulerian cycle. The cost of the eulerian cycle equals $2 \cdot OPT$ by edge double.

Construct a hamiltonian cycle by short cutting steiner vertices previously visited. If the question was how to extend a railway network in a cheap manner for peripheral stations, this is an answer.

To get a hamiltonian path remove one edge of the cycle. We have found an MST on the terminal vertices. A hamiltonian path does not increase the cost, because of the triangle inequality and removing edges. The resulting MST on the terminal vertices cost $\leq 2 \cdot OPT$ and the bound is tight.

Looking back on $K_4$ we have to calculate the solution from $G’$ back to the original $G$ instance by reapplying the shortest path edges for $G’$ to the edges in $G$.

By including the optional Steiner vertice we have found a better MST compared to the Kruskal algorithm.

Special thanks to Prof. Hans Kellerer (University of Graz) for teaching.