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Globally convergent homotopy algorithms for nonlinear systems of equations

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Abstract

Probability-one homotopy methods are a class of algorithms for solving nonlinear systems of equations that are accurate, robust, and converge from an arbitrary starting point almost surely. These new globally convergent homotopy techniques have been successfully applied to solve Brouwer fixed point problems, polynomial systems of equations, constrained and unconstrained optimization problems, discretizations of nonlinear two-point boundary value problems based on shooting, finite differences, collocation, and finite elements, and finite difference, collocation, and Galerkin approximations to nonlinear partial differential equations. This paper introduces, in a tutorial fashion, the theory of globally convergent homotopy algorithms, deseribes some computer algorithms and mathematical software, and presents several nontrivial engineering applications.

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Authors and Affiliations

  1. Department of Computer Science, Virginia Polytechnic Institute & State University, 24061, Blacksburg, VA, USA

    Layne T. Watson

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This work was supported in part by DOE Grant DE-FG05-88ER25068, NASA Grant NAG-1-1079, and AFOSR Grant 89-0497.

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Watson, L.T. Globally convergent homotopy algorithms for nonlinear systems of equations. Nonlinear Dyn 1, 143–191 (1990). https://doi.org/10.1007/BF01857785

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