“…Now we are going to relate this parametrization u = Wk('~ ) to that defined by (1), (5), (6). In order to avoid confusion we denote the parameter introduced by (5), (6) for all 1a1<62.…”
Section: The Secant Length Parametrizationmentioning
confidence: 98%
“…2 where the parameter is called a. The parameter v occurring in (5) is up to sign the secant length between the basic point u k and the point u = Wk(Z ) defined by (1), (5), (6). It represents best the steplength in the k-th step of the path following algorithm.…”
Summary. A possible way for parametrizing the solution path of the nonlinear system H(u)=0, H: IU+~R" consists of using the secant length as parameter. This idea leads to a quadratic constraint by which the parameter is introduced. A Newton-like method for computing the solution for a given parameter is proposed where the nonlinear system is linearized at each iterate, but the quadratic parametrizing equation is exactly satisfied. The local Q-quadratic convergence of the method is proved and some hints for implementing the algorithm are given
“…Now we are going to relate this parametrization u = Wk('~ ) to that defined by (1), (5), (6). In order to avoid confusion we denote the parameter introduced by (5), (6) for all 1a1<62.…”
Section: The Secant Length Parametrizationmentioning
confidence: 98%
“…2 where the parameter is called a. The parameter v occurring in (5) is up to sign the secant length between the basic point u k and the point u = Wk(Z ) defined by (1), (5), (6). It represents best the steplength in the k-th step of the path following algorithm.…”
Summary. A possible way for parametrizing the solution path of the nonlinear system H(u)=0, H: IU+~R" consists of using the secant length as parameter. This idea leads to a quadratic constraint by which the parameter is introduced. A Newton-like method for computing the solution for a given parameter is proposed where the nonlinear system is linearized at each iterate, but the quadratic parametrizing equation is exactly satisfied. The local Q-quadratic convergence of the method is proved and some hints for implementing the algorithm are given
“…The approach taken here extends to a Banach space setting the results of [13] where a basic procedure was developed that produces characterizations for generalized turning points in finite dimensions. Our approach is related to other characterization equations in special cases such as given in [2,15] and [22]. See [13] and [14].…”
Section: Introductionmentioning
confidence: 99%
“…See, for example, Doedel [9]. The iterative solution of these characterization equations does not require the iterates to lie on the solution manifold as was done in algorithms for simple turning points given by Simpson [26] and also P6nisch and Schwetlick [22].…”
Summary.A procedure is given that generates characterizations of singular manifolds for mildly nonlinear mappings between Banach spaces. This characterization is used to develop a method for determining generalized turning points by using projection methods as a discretization. Applications are given to parameter dependent two-point boundary value problems. In particular, collocation at Gauss points is shown to achieve superconvergence in approximating the parameter at simple turning points.
“…[23], [24], [27], [29], [31], [32], [34], [39], [40] and the references given there). We shall not go into details here but sketch only some of the principal features of these methods as they apply in the present setting.…”
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