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The QoS Steiner Tree Problem asks for the most cost efficient way to multicast multimedia to a heterogeneous collection of users with different data consumption rates. We assume that the cost of using a link is not constant but rather depends on the maximum bandwidth routed through the link. Formally, given a graph with costs on the edges, a source node and a set of terminal nodes, each one with a bandwidth requirement, the goal is to find a Steiner tree containing the source, and the cheapest assignment of bandwidth to each of its edges so that each source-to-terminal path in the tree has bandwidth at least as large as the bandwidth required by the terminal. Our main contributions are: (1) New flow-based integer linear program formulation for the problem; (2) First implementation of 4.311 primal-dual constant factor approximation algorithm; (3) an extensive experimental study of the new heuristics and of several previously proposed algorithms.

The Steiner tree problem is a classical, well-studied, $\mathcal{NP}$-hard optimization problem. Here we are given an undirected graph $G=(V,E)$, a subset $R$ of $V$ of terminals, and non-negative costs $c_e$ for all edges $e$ in $E$. A feasible Steiner tree for a given instance is a tree $T$ in $G$ that spans all terminals in $R$. The goal is to compute a feasible Steiner tree of smallest cost. In this thesis we will focus on approximation algorithms for this problem: a $c$-approximation algorithm is an algorithm that returns a tree of cost at most $c$ times that of an optimum solution for any given input instance. <br /><br /> In a series of papers throughout the last decade, the approximation guarantee $c$ for the Steiner tree problem has been improved to the currently best known value of 1. 55 (Robins, Zelikovsky). Robins' and Zelikovsky's algorithm as well as most of its predecessors are greedy algorithms. <br /><br /> Apart from algorithmic improvements, there also has been substantial work on obtaining tight linear-programming relaxations for the Steiner tree problem. Many undirected and directed formulations have been proposed in the course of the last 25 years; their use, however, is to this point mostly restricted to the field of exact optimization. There are few examples of algorithms for the Steiner tree problem that make use of these LP relaxations. The best known such algorithm for general graphs is a 2-approximation (for the more general Steiner forest problem) due to Agrawal, Klein and Ravi. Their analysis is tight as the LP-relaxation used in their work is known to be weak: it has an IP/LP gap of approximately 2. <br /><br /> Most recent efforts to obtain algorithms for the Steiner tree problem that are based on LP-relaxations has focused on directed relaxations. In this thesis we present an undirected relaxation and show that the algorithm of Robins and Zelikovsky returns a Steiner tree whose cost is at most 1. 55 times its optimum solution value. In fact, we show that this algorithm can be viewed as a primal-dual algorithm. <br /><br/> The Steiner forest problem is a generalization of the Steiner tree problem. In the problem, instead of only one set of terminals, we are given more than one terminal set. An feasible Steiner forest is a forest that connects all terminals in the same terminal set for each terminal set. The goal is to find a minimum cost feasible Steiner forest. In this thesis, a new set of facet defining inequalities for the polyhedra of the Steiner forest is introduced.

This thesis examines the (geometric) Steiner tree problem: Given a set of points P in the plane, find a shortest tree interconnecting all points in P, with the possibility of adding points outside P, called the Steiner points, as additional vertices of the tree. The Steiner tree problem has been studied in different metric spaces. In this thesis, we study the problem in Euclidean and rectilinear metrics. One of the most natural heuristics for the Steiner tree problem is to use a minimum spanning tree, which can be found in O(nlogn) time . The performance ratio of this heuristic is given by the Steiner ratio, which is defined as the minimum possible ratio between the lengths of a minimum Steiner tree and a minimum spanning tree. We survey the background literature on the Steiner ratio and study the generalization of the Steiner ratio to the case of obstacles. We introduce the concept of an anchored Steiner tree: an obstacle-avoiding Steiner tree in which the Steiner points are only allowed at obstacle corners. We define the obstacle-avoiding Steiner ratio as the ratio of the length of an obstacle-avoiding minimum Steiner tree to that of an anchored obstacle-avoiding minimum Steiner tree. We prove that, for the rectilinear metric, the obstacle-avoiding Steiner ratio is equal to the traditional (obstacle-free) Steiner ratio. We conjecture that this is also the case for the Euclidean metric and we prove this conjecture for three points and any number of obstacles.

The Steiner tree problem is a classical, well-studied, $\mathcal{NP}$-hard optimization problem. Here we are given an undirected graph $G=(V,E)$, a subset $R$ of $V$ of terminals, and non-negative costs $c_e$ for all edges $e$ in $E$. A feasible Steiner tree for a given instance is a tree $T$ in $G$ that spans all terminals in $R$. The goal is to compute a feasible Steiner tree of smallest cost. In this thesis we will focus on approximation algorithms for this problem: a $c$-approximation algorithm is an algorithm that returns a tree of cost at most $c$ times that of an optimum solution for any given input instance. <br /><br /> In a series of papers throughout the last decade, the approximation guarantee $c$ for the Steiner tree problem has been improved to the currently best known value of 1. 55 (Robins, Zelikovsky). Robins' and Zelikovsky's algorithm as well as most of its predecessors are greedy algorithms. <br /><br /> Apart from algorithmic improvements, there also has been substantial work on obtaining tight linear-programming relaxations for the Steiner tree problem. Many undirected and directed formulations have been proposed in the course of the last 25 years; their use, however, is to this point mostly restricted to the field of exact optimization. There are few examples of algorithms for the Steiner tree problem that make use of these LP relaxations. The best known such algorithm for general graphs is a 2-approximation (for the more general Steiner forest problem) due to Agrawal, Klein and Ravi. Their analysis is tight as the LP-relaxation used in their work is known to be weak: it has an IP/LP gap of approximately 2. <br /><br /> Most recent efforts to obtain algorithms for the Steiner tree problem that are based on LP-relaxations has focused on directed relaxations. In this thesis we present an undirected relaxation and show that the algorithm of Robins and Zelikovsky returns a Steiner tree whose cost is at most 1. 55 times its optimum solution value. In fact, we show that this algorithm can be viewed as a primal-dual algorithm. <br /><br/> The Steiner forest problem is a generalization of the Steiner tree problem. In the problem, instead of only one set of terminals, we are given more than one terminal set. An feasible Steiner forest is a forest that connects all terminals in the same terminal set for each terminal set. The goal is to find a minimum cost feasible Steiner forest. In this thesis, a new set of facet defining inequalities for the polyhedra of the Steiner forest is introduced.

This thesis examines the (geometric) Steiner tree problem: Given a set of points P in the plane, find a shortest tree interconnecting all points in P, with the possibility of adding points outside P, called the Steiner points, as additional vertices of the tree. The Steiner tree problem has been studied in different metric spaces. In this thesis, we study the problem in Euclidean and rectilinear metrics. One of the most natural heuristics for the Steiner tree problem is to use a minimum spanning tree, which can be found in O(nlogn) time . The performance ratio of this heuristic is given by the Steiner ratio, which is defined as the minimum possible ratio between the lengths of a minimum Steiner tree and a minimum spanning tree. We survey the background literature on the Steiner ratio and study the generalization of the Steiner ratio to the case of obstacles. We introduce the concept of an anchored Steiner tree: an obstacle-avoiding Steiner tree in which the Steiner points are only allowed at obstacle corners. We define the obstacle-avoiding Steiner ratio as the ratio of the length of an obstacle-avoiding minimum Steiner tree to that of an anchored obstacle-avoiding minimum Steiner tree. We prove that, for the rectilinear metric, the obstacle-avoiding Steiner ratio is equal to the traditional (obstacle-free) Steiner ratio. We conjecture that this is also the case for the Euclidean metric and we prove this conjecture for three points and any number of obstacles.

In the Directed Steiner Tree problem, we are given a directed graph G = (V,E) with edge costs, a root vertex r ∈ V, and a terminal set X ⊆ V . The goal is to find the cheapest subset of edges that contains an r-t path for every terminal t ∈ X. The only known polylogarithmic approximations for Directed Steiner Tree run in quasi-polynomial time and the best polynomial time approximations only achieve a guarantee of O(|X|^ε) for any constant ε > 0. Furthermore, the integrality gap of a natural LP relaxation can be as bad as Ω(√|X|).  We demonstrate that l rounds of the Sherali-Adams hierarchy suffice to reduce the integrality gap of a natural LP relaxation for Directed Steiner Tree in l-layered graphs from Ω( k) to O(l · log k) where k is the number of terminals. This is an improvement over Rothvoss’ result that 2l rounds of the considerably stronger Lasserre SDP hierarchy reduce the integrality gap of a similar formulation to O(l · log k). We also observe that Directed Steiner Tree instances with 3 layers of edges have only an O(logk) integrality gap bound in the standard LP relaxation, complementing the fact that the gap can be as large as Ω(√k) in graphs with 4 layers. Finally, we consider quasi-bipartite instances of Directed Steiner Tree meaning no edge in E connects two Steiner nodes V − (X ∪ {r}). By a simple reduction from Set Cover, it is still NP-hard to approximate quasi-bipartite instances within a ratio better than O(log|X|). We present a polynomial-time O(log |X|)-approximation for quasi-bipartite instances of Directed Steiner Tree. Our approach also bounds the integrality gap of the natural LP relaxation by the same quantity. A novel feature of our algorithm is that it is based on the primal-dual framework, which typically does not result in good approximations for network design problems in directed graphs.

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Given a finite set K of terminals in the plane. A rectilinear Steiner minimum tree for K (RST) is a tree which interconnects among these terminals using only horizontal and vertical lines of shortest possible length containing Steiner point. We show the complexity of RST i.e. belongs to NP-complete. Moreover we present an approximative method of determining the solution of RST problem proposed by Sanjeev Arora in 1996, Arora's Approximation Scheme. This algorithm has time complexity polynomial in the number of terminals for a fixed performance ratio 1 + Epsilon.