On the solution of volterra integral equations of the first kind

Department of Applied Mathematics California Institute of Technology 91109 Pasadena California
Numerische Mathematik (Impact Factor: 1.61). 01/1973; 21(1):22-32. DOI: 10.1007/BF01436183


Two classes of high order finite difference methods for first kind Volterra integral equations are constructed. The methods are shown to be convergent and numerically stable.

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    • "Therefore the solution of integral-functional equation (7) can be non unique. Let us follow paper [5] and search for the asymptotic approximation of a particular solution of the inhomogeneous equation (7) as following polynomial "
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    ABSTRACT: Sufficient conditions for existence and uniqueness of the solution of the Volterra integral equations of the first kind with piecewise continuous kernels are derived in framework of Sobolev-Schwartz distribution theory. The asymptotic approximation of the parametric family of generalized solutions is constructed. The method for the solution's regular part refinement is proposed using the successive approximations method.
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    ABSTRACT: The numerical differentiation of data divides naturally into two distinct problems: the differentiation of exact data, and the differentiation of non-exact (experimental) data. In this paper, we examine the latter. Because methods developed for exact data are based on abstract formalisms which are independent of the structure within the data, they prove, except for the regularization procedure of Cullum, to be unsatisfactory for non-exact data. We therefore adopt the point of view that satisfactory methods for non-exact data must take the structure within the data into account in some natural way, and use the concepts of regression and spectrum analysis as a basis for the development of such methods. The regression procedure is used when either the structure within the non-exact data is known on independent grounds, or the assumptions which underlie the spectrum analysis procedure [viz., stationarity of the (detrended) data] do not apply. In this latter case, the data could be modelled using splines. The spectrum analysis procedure is used when the structure within the nonexact data (or a suitable transformation of it, where the transformation can be differentiated exactly) behaves as if it were generated by a stationary stochastic process. By proving that the regularization procedure of Cullum is equivalent to a certain spectrum analysis procedure, we derive a fast Fourier transform implementation for regularization (based on this equivalence) in which an acceptable value of the regularization parameter is estimated directly from a time series formulation based on this equivalence. Compared with the regularization procedure, which involvesO(n 3) operations (wheren is the number of data points), the fast Fourier transform implementation only involvesO(n logn).
    Numerische Mathematik 01/1972; 22(3). DOI:10.1007/BF01436965 · 1.61 Impact Factor
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