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“Potential Interval of Root” of Nonlinear Equation: Labeling Algorithm

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Multi-disciplinary Trends in Artificial Intelligence (MIWAI 2014)

Abstract

Novel Sequence Generating Algorithm (SGA) and Potential Interval Qualifier Algorithm (PIQA) are designed to classify potential interval estimates of a given nonlinear equation into intervals possessing roots and intervals containing extrema. Using trisection method, SGA is developed to generate conjugate pair of sequences that converge to a point in the interval. Further, PIQA qualifies each interval into interval enclosing a root or interval containing extrema. If the interval contains a root, the multiplicity of root is also obtained. The proposed methodologies have been implemented and demonstrated through a set of benchmark functions to illustrate the effectiveness.

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References

  1. Numerical Recipes: The Art of Scientific Computing, 3rd edn. Cambridge Univ. Press

    Google Scholar 

  2. Holland, J.H.: Adaption in natural and artificial systems: An introductory analysis with applications to biology, control, and artificial intelligence. University of Michigan Press

    Google Scholar 

  3. Goldberg, D.E.: Genetic Algorithms in Search, Optimization and Machine Learning. Addison-Wesley Publishing

    Google Scholar 

  4. Castillo, O., Melin, P., Pedrycz, W.: Soft Computing for Hybrid Intelligent Systems

    Google Scholar 

  5. Pourjafari, E., Mojallali, H.: Solving nonlinear equations systems with a new approach based on Invasive weed optimization algorithm and clustering. Swarm and Evolutionary Computation 4, 33–43 (2012)

    Article  Google Scholar 

  6. Nadimpalli, V.L.V., Wankar, R., Chillarige, R.R.: A Novel Genetic Algorithmic Approach for Computing Real Roots of a Nonlinear Equation. In: Proceedings of EVO Star 2014. LNCS (in press, 2014)

    Google Scholar 

  7. Deb, K.: Multi-objective Optimization Using Evolutionary Algorithms. Wiley (2001)

    Google Scholar 

  8. Traub, J.F.: Iterative Methods for the Solution of Equations. Prentice Hall, Englewood (1964)

    MATH  Google Scholar 

  9. Espelid, T.O.: On the behavior of the secant method near a multiple root. BIT I1, 112–115 (1972)

    Article  MathSciNet  Google Scholar 

  10. Parida, P.K., Gupta, D.K.: An improved method for finding multiple roots and it’s multiplicity of nonlinear equations in R*. Applied Mathematics and Computation 202, 498–503 (2008)

    Article  MATH  MathSciNet  Google Scholar 

  11. Yun, B.I.: Iterative methods for solving nonlinear equations with finitely many zeros in an interval. Journal of Computational and Applied Mathematics 236, 3308–3318 (2012)

    Article  MATH  MathSciNet  Google Scholar 

  12. King, R.F.: A secant method for multiple roots. BIT 17, 321–328 (1977)

    Article  MATH  Google Scholar 

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© 2014 Springer International Publishing Switzerland

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Nadimpalli, V.L.V., Wankar, R., Chillarige, R.R. (2014). “Potential Interval of Root” of Nonlinear Equation: Labeling Algorithm. In: Murty, M.N., He, X., Chillarige, R.R., Weng, P. (eds) Multi-disciplinary Trends in Artificial Intelligence. MIWAI 2014. Lecture Notes in Computer Science(), vol 8875. Springer, Cham. https://doi.org/10.1007/978-3-319-13365-2_1

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  • DOI: https://doi.org/10.1007/978-3-319-13365-2_1

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-13364-5

  • Online ISBN: 978-3-319-13365-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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