9,165 research outputs found
k-PathA: k-shortest Path Algorithm
One important aspect of computational systems biology
includes the identification and analysis of functional response
networks within large biochemical networks. These functional
response networks represent the response of a biological system
under a particular experimental condition which can be used to
pinpoint critical biological processes.
For this purpose, we have developed a novel algorithm to calculate
response networks as scored/weighted sub-graphs spanned by
k-shortest simple (loop free) paths. The k-shortest simple path
algorithm is based on a forward/backward chaining approach
synchronized between pairs of processors. The algorithm scales
linear with the number of processors used. The algorithm
implementation is using a Linux cluster platform, MPI lam
and mpiJava messaging as well as the Java language for the
application.
The algorithm is performed on a hybrid human network consisting
of 45,041 nodes and 438,567 interactions together with
gene expression information obtained from human cell-lines
infected by influenza virus. Its response networks show the early
innate immune response and virus triggered processes within
human epithelial cells. Especially under the imminent threat of
a pandemic caused by novel influenza strains, such as the current
H1N1 strain, these analyses are crucial for a comprehensive
understanding of molecular processes during early phases of
infection. Such a systems level understanding may aid in the
identification of therapeutic markers and in drug development
for diagnosis and finally prevention of a potentially dangerous
disease
An adaptive distributed Dijkstra shortest path algorithm
Cover title.Includes bibliographical references.Supported in part by the Codex Corporation. Supported in part by the Army Research Office. DAAL03-86-K-0171Pierre A. Humblet
Calculating path algorithms
AbstractA calculational derivation is given of two abstract path algorithms. The first is an all-pairs algorithm, two well-known instances of which are Warshall's (reachability) algorithm and Floyd's shortest-path algorithm; instances of the second are Dijkstra's shortest-path algorithm and breadth-first/depth-first search of a directed graph. The basis for the derivations is the algebra of regular languages
Smoothed Analysis of the Successive Shortest Path Algorithm
The minimum-cost flow problem is a classic problem in combinatorial
optimization with various applications. Several pseudo-polynomial, polynomial,
and strongly polynomial algorithms have been developed in the past decades, and
it seems that both the problem and the algorithms are well understood. However,
some of the algorithms' running times observed in empirical studies contrast
the running times obtained by worst-case analysis not only in the order of
magnitude but also in the ranking when compared to each other. For example, the
Successive Shortest Path (SSP) algorithm, which has an exponential worst-case
running time, seems to outperform the strongly polynomial Minimum-Mean Cycle
Canceling algorithm.
To explain this discrepancy, we study the SSP algorithm in the framework of
smoothed analysis and establish a bound of for the number of
iterations, which implies a smoothed running time of ,
where and denote the number of nodes and edges, respectively, and
is a measure for the amount of random noise. This shows that worst-case
instances for the SSP algorithm are not robust and unlikely to be encountered
in practice. Furthermore, we prove a smoothed lower bound of for the number of iterations of the SSP algorithm, showing
that the upper bound cannot be improved for .Comment: A preliminary version has been presented at SODA 201
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