159 research outputs found
Probabilistic Analysis of Facility Location on Random Shortest Path Metrics
The facility location problem is an NP-hard optimization problem. Therefore,
approximation algorithms are often used to solve large instances. Such
algorithms often perform much better than worst-case analysis suggests.
Therefore, probabilistic analysis is a widely used tool to analyze such
algorithms. Most research on probabilistic analysis of NP-hard optimization
problems involving metric spaces, such as the facility location problem, has
been focused on Euclidean instances, and also instances with independent
(random) edge lengths, which are non-metric, have been researched. We would
like to extend this knowledge to other, more general, metrics.
We investigate the facility location problem using random shortest path
metrics. We analyze some probabilistic properties for a simple greedy heuristic
which gives a solution to the facility location problem: opening the
cheapest facilities (with only depending on the facility opening
costs). If the facility opening costs are such that is not too large,
then we show that this heuristic is asymptotically optimal. On the other hand,
for large values of , the analysis becomes more difficult, and we
provide a closed-form expression as upper bound for the expected approximation
ratio. In the special case where all facility opening costs are equal this
closed-form expression reduces to or or even
if the opening costs are sufficiently small.Comment: A preliminary version accepted to CiE 201
Thermal noise suppression: how much does it cost?
In order to stabilize the behavior of noisy systems, confining it around a
desirable state, an effort is required to suppress the intrinsic noise. This
noise suppression task entails a cost. For the important case of thermal noise
in an overdamped system, we show that the minimum cost is achieved when the
system control parameters are held constant: any additional deterministic or
random modulation produces an increase of the cost. We discuss the implications
of this phenomenon for those overdamped systems whose control parameters are
intrinsically noisy, presenting a case study based on the example of a Brownian
particle optically trapped in an oscillating potential.Comment: 6 page
Misty Mountain clustering: application to fast unsupervised flow cytometry gating
<p>Abstract</p> <p>Background</p> <p>There are many important clustering questions in computational biology for which no satisfactory method exists. Automated clustering algorithms, when applied to large, multidimensional datasets, such as flow cytometry data, prove unsatisfactory in terms of speed, problems with local minima or cluster shape bias. Model-based approaches are restricted by the assumptions of the fitting functions. Furthermore, model based clustering requires serial clustering for all cluster numbers within a user defined interval. The final cluster number is then selected by various criteria. These supervised serial clustering methods are time consuming and frequently different criteria result in different optimal cluster numbers. Various unsupervised heuristic approaches that have been developed such as affinity propagation are too expensive to be applied to datasets on the order of 10<sup>6 </sup>points that are often generated by high throughput experiments.</p> <p>Results</p> <p>To circumvent these limitations, we developed a new, unsupervised density contour clustering algorithm, called Misty Mountain, that is based on percolation theory and that efficiently analyzes large data sets. The approach can be envisioned as a progressive top-down removal of clouds covering a data histogram relief map to identify clusters by the appearance of statistically distinct peaks and ridges. This is a parallel clustering method that finds every cluster after analyzing only once the cross sections of the histogram. The overall run time for the composite steps of the algorithm increases linearly by the number of data points. The clustering of 10<sup>6 </sup>data points in 2D data space takes place within about 15 seconds on a standard laptop PC. Comparison of the performance of this algorithm with other state of the art automated flow cytometry gating methods indicate that Misty Mountain provides substantial improvements in both run time and in the accuracy of cluster assignment.</p> <p>Conclusions</p> <p>Misty Mountain is fast, unbiased for cluster shape, identifies stable clusters and is robust to noise. It provides a useful, general solution for multidimensional clustering problems. We demonstrate its suitability for automated gating of flow cytometry data.</p
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