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https://dx.doi.org/10.48550/ar...
Article . 2009
License: CC BY
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Computing Least Fixed Points of Probabilistic Systems of Polynomials

Authors: Esparza, Javier; Gaiser, Andreas; Kiefer, Stefan;

Computing Least Fixed Points of Probabilistic Systems of Polynomials

Abstract

We study systems of equations of the form X1 = f1(X1, ..., Xn), ..., Xn = fn(X1, ..., Xn), where each fi is a polynomial with nonnegative coefficients that add up to 1. The least nonnegative solution, say mu, of such equation systems is central to problems from various areas, like physics, biology, computational linguistics and probabilistic program verification. We give a simple and strongly polynomial algorithm to decide whether mu=(1, ..., 1) holds. Furthermore, we present an algorithm that computes reliable sequences of lower and upper bounds on mu, converging linearly to mu. Our algorithm has these features despite using inexact arithmetic for efficiency. We report on experiments that show the performance of our algorithms.

Published in the Proceedings of the 27th International Symposium on Theoretical Aspects of Computer Science (STACS). Technical Report is also available via arxiv.org

Keywords

Computing fixed points, FOS: Computer and information sciences, F.2.1; G.3, branching processes, G.3, Numerical Analysis (math.NA), 004, 510, Computer Science - Data Structures and Algorithms, FOS: Mathematics, Data Structures and Algorithms (cs.DS), Mathematics - Numerical Analysis, F.2.1, stochastic models, numerical approximation, ddc: ddc:004

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
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