Result: Computing Least Fixed Points of Probabilistic Systems of Polynomials
Title:
Computing Least Fixed Points of Probabilistic Systems of Polynomials
Authors:
Contributors:
Institut für Informatik (LRR-TUM), Technische Universität Munchen - Technical University Munich - Université Technique de Munich (TUM), Inria Nancy Grand Est & Loria, Jean-Yves Marion and Thomas Schwentick
Source:
27th International Symposium on Theoretical Aspects of Computer Science - STACS 2010. :359-370
Publisher Information:
HAL CCSD, 2010.
Publication Year:
2010
Collection:
collection:STACS2010
Subject Terms:
computing fixed points, numerical approximation, stochastic model, branching processes, ACM: F.: Theory of Computation, F.2: ANALYSIS OF ALGORITHMS AND PROBLEM COMPLEXITY, F.2.1: Numerical Algorithms and Problems, ACM: G.: Mathematics of Computing, G.3: PROBABILITY AND STATISTICS, [INFO.INFO-DS]Computer Science [cs], Data Structures and Algorithms [cs.DS], [INFO.INFO-NA]Computer Science [cs], Numerical Analysis [cs.NA]
Subject Geographic:
Original Identifier:
HAL:
Document Type:
Conference
conferenceObject<br />Conference papers
Language:
English
Access URL:
Rights:
info:eu-repo/semantics/OpenAccess
Accession Number:
edshal.inria.00455344v1
Database:
HAL
Further Information
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.