203 research outputs found
Reduction techniques for the prize collecting Steiner tree problem and the maximumâweight connected subgraph problem
The concept of reduction has frequently distinguished itself as a pivotal ingredient of exact solving approaches for the Steiner tree problem in graphs. In this article we broaden the focus and consider reduction techniques for three Steiner problem variants that have been extensively discussed in the literature and entail various practical applications: The prizeâcollecting Steiner tree problem, the rooted prizeâcollecting Steiner tree problem and the maximumâweight connected subgraph problem. By introducing and subsequently deploying numerous new reduction methods, we are able to drastically decrease the size of a large number of benchmark instances, already solving more than 90% of them to optimality. Furthermore, we demonstrate the impact of these techniques on exact solving, using the example of the stateâofâtheâart Steiner problem solver SCIPâJack
Approximation Algorithms for Union and Intersection Covering Problems
In a classical covering problem, we are given a set of requests that we need
to satisfy (fully or partially), by buying a subset of items at minimum cost.
For example, in the k-MST problem we want to find the cheapest tree spanning at
least k nodes of an edge-weighted graph. Here nodes and edges represent
requests and items, respectively.
In this paper, we initiate the study of a new family of multi-layer covering
problems. Each such problem consists of a collection of h distinct instances of
a standard covering problem (layers), with the constraint that all layers share
the same set of requests. We identify two main subfamilies of these problems: -
in a union multi-layer problem, a request is satisfied if it is satisfied in at
least one layer; - in an intersection multi-layer problem, a request is
satisfied if it is satisfied in all layers. To see some natural applications,
consider both generalizations of k-MST. Union k-MST can model a problem where
we are asked to connect a set of users to at least one of two communication
networks, e.g., a wireless and a wired network. On the other hand, intersection
k-MST can formalize the problem of connecting a subset of users to both
electricity and water.
We present a number of hardness and approximation results for union and
intersection versions of several standard optimization problems: MST, Steiner
tree, set cover, facility location, TSP, and their partial covering variants
The Fast Heuristic Algorithms and Post-Processing Techniques to Design Large and Low-Cost Communication Networks
It is challenging to design large and low-cost communication networks. In
this paper, we formulate this challenge as the prize-collecting Steiner Tree
Problem (PCSTP). The objective is to minimize the costs of transmission routes
and the disconnected monetary or informational profits. Initially, we note that
the PCSTP is MAX SNP-hard. Then, we propose some post-processing techniques to
improve suboptimal solutions to PCSTP. Based on these techniques, we propose
two fast heuristic algorithms: the first one is a quasilinear time heuristic
algorithm that is faster and consumes less memory than other algorithms; and
the second one is an improvement of a stateof-the-art polynomial time heuristic
algorithm that can find high-quality solutions at a speed that is only inferior
to the first one. We demonstrate the competitiveness of our heuristic
algorithms by comparing them with the state-of-the-art ones on the largest
existing benchmark instances (169 800 vertices and 338 551 edges). Moreover, we
generate new instances that are even larger (1 000 000 vertices and 10 000 000
edges) to further demonstrate their advantages in large networks. The
state-ofthe-art algorithms are too slow to find high-quality solutions for
instances of this size, whereas our new heuristic algorithms can do this in
around 6 to 45s on a personal computer. Ultimately, we apply our
post-processing techniques to update the bestknown solution for a notoriously
difficult benchmark instance to show that they can improve near-optimal
solutions to PCSTP. In conclusion, we demonstrate the usefulness of our
heuristic algorithms and post-processing techniques for designing large and
low-cost communication networks
Node-Weighted Prize Collecting Steiner Tree and Applications
The Steiner Tree problem has appeared in the Karp's list of the first 21 NP-hard problems and is well known as one of the most fundamental problems in Network Design area. We study the Node-Weighted version of the Prize
Collecting Steiner Tree problem.
In this problem, we are given a simple graph with a cost and penalty value associated with each node. Our
goal is to find a subtree T of the graph minimizing the cost of the
nodes in T plus penalty of the nodes not in T. By a reduction
from set cover problem it can be easily shown that the problem cannot be approximated in polynomial time within factor of (1-o(1))ln n unless NP has quasi-polynomial time algorithms, where n is the number of vertices of the graph.
Moss and Rabani claimed an O(log n)-approximation algorithm for the problem using a Primal-Dual approach in their STOC'01 paper \cite{moss2001}. We show that their algorithm is incorrect by providing a counter example in which there is an O(n) gap between the dual solution constructed by their algorithm and the optimal solution. Further, evidence is given that their algorithm probably does not have a simple fix. We propose a new algorithm which is more involved and
introduces novel ideas in primal dual approach for network design problems. Also, our algorithm is a Lagrangian Multiplier Preserving algorithm and we show how this property can be utilized to design an O(log n)-approximation algorithm for the Node-Weighted Quota Steiner Tree problem
using the Lagrangian Relaxation method.
We also show an application of the Node Weighted Quota Steiner Tree problem in designing algorithm with better approximation factor for
Technology Diffusion problem, a problem proposed by Goldberg and Liu
in \cite{goldberg2012} (SODA 2013). In Technology Diffusion, we are given a graph G and a threshold θ(v) associated with each vertex v and we are seeking a set of initial nodes called the seed set.
Technology Diffusion is a dynamic process defined over time in which each vertex is either active or inactive. The vertices in the seed set
are initially activated and each other vertex v gets activated whenever there are at least θ(v) active nodes connected to
v through other active nodes. The Technology Diffusion problem asks to
find the minimum seed set activating all nodes. Goldberg
and Liu gave an O(rllog n)-approximation algorithm for the problem where
r and l are the diameter of G and the number of distinct threshold values, respectively. We improve the approximation factor
to O(min{r,l}log n) by establishing a close connection between the problem and the Node Weighted Quota Steiner Tree problem
Identifying functional modules in proteinâprotein interaction networks: an integrated exact approach
Motivation: With the exponential growth of expression and proteinâprotein interaction (PPI) data, the frontier of research in systems biology shifts more and more to the integrated analysis of these large datasets. Of particular interest is the identification of functional modules in PPI networks, sharing common cellular function beyond the scope of classical pathways, by means of detecting differentially expressed regions in PPI networks. This requires on the one hand an adequate scoring of the nodes in the network to be identified and on the other hand the availability of an effective algorithm to find the maximally scoring network regions. Various heuristic approaches have been proposed in the literature
Improved Approximation Algorithms for (Budgeted) Node-weighted Steiner Problems
Moss and Rabani[12] study constrained node-weighted Steiner tree problems
with two independent weight values associated with each node, namely, cost and
prize (or penalty). They give an O(log n)-approximation algorithm for the
prize-collecting node-weighted Steiner tree problem (PCST). They use the
algorithm for PCST to obtain a bicriteria (2, O(log n))-approximation algorithm
for the Budgeted node-weighted Steiner tree problem. Their solution may cost up
to twice the budget, but collects a factor Omega(1/log n) of the optimal prize.
We improve these results from at least two aspects.
Our first main result is a primal-dual O(log h)-approximation algorithm for a
more general problem, prize-collecting node-weighted Steiner forest, where we
have (h) demands each requesting the connectivity of a pair of vertices. Our
algorithm can be seen as a greedy algorithm which reduces the number of demands
by choosing a structure with minimum cost-to-reduction ratio. This natural
style of argument (also used by Klein and Ravi[10] and Guha et al.[8]) leads to
a much simpler algorithm than that of Moss and Rabani[12] for PCST.
Our second main contribution is for the Budgeted node-weighted Steiner tree
problem, which is also an improvement to [12] and [8]. In the unrooted case, we
improve upon an O(log^2(n))-approximation of [8], and present an O(log
n)-approximation algorithm without any budget violation. For the rooted case,
where a specified vertex has to appear in the solution tree, we improve the
bicriteria result of [12] to a bicriteria approximation ratio of (1+eps, O(log
n)/(eps^2)) for any positive (possibly subconstant) (eps). That is, for any
permissible budget violation (1+eps), we present an algorithm achieving a
tradeoff in the guarantee for prize. Indeed, we show that this is almost tight
for the natural linear-programming relaxation used by us as well as in [12].Comment: To appear in ICALP 201
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