935 resultados para ordinary differential equations
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Includes bibliographies (p. 11).
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Contiene: Vol. supplementary.
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Mode of access: Internet.
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At head of title: Office of Naval Research, Contract NONR-1858(04), Project NRO43-942.
Regular singular points of a system of homogeneous linear differential equations of the first order.
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"From Proceedings of the American Academy of Arts and Sciences, v.38, no. 9, Oct. 1902."
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Cover title.
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Bibliography: leaf [77]
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Mode of access: Internet.
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Mode of access: Internet.
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First issued in 25 parts, July 15, 1836, to June 1, 1842.
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Mode of access: Internet.
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Mode of access: Internet.
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Mode of access: Internet.
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In this paper we construct implicit stochastic Runge-Kutta (SRK) methods for solving stochastic differential equations of Stratonovich type. Instead of using the increment of a Wiener process, modified random variables are used. We give convergence conditions of the SRK methods with these modified random variables. In particular, the truncated random variable is used. We present a two-stage stiffly accurate diagonal implicit SRK (SADISRK2) method with strong order 1.0 which has better numerical behaviour than extant methods. We also construct a five-stage diagonal implicit SRK method and a six-stage stiffly accurate diagonal implicit SRK method with strong order 1.5. The mean-square and asymptotic stability properties of the trapezoidal method and the SADISRK2 method are analysed and compared with an explicit method and a semi-implicit method. Numerical results are reported for confirming convergence properties and for comparing the numerical behaviour of these methods.
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This paper gives a review of recent progress in the design of numerical methods for computing the trajectories (sample paths) of solutions to stochastic differential equations. We give a brief survey of the area focusing on a number of application areas where approximations to strong solutions are important, with a particular focus on computational biology applications, and give the necessary analytical tools for understanding some of the important concepts associated with stochastic processes. We present the stochastic Taylor series expansion as the fundamental mechanism for constructing effective numerical methods, give general results that relate local and global order of convergence and mention the Magnus expansion as a mechanism for designing methods that preserve the underlying structure of the problem. We also present various classes of explicit and implicit methods for strong solutions, based on the underlying structure of the problem. Finally, we discuss implementation issues relating to maintaining the Brownian path, efficient simulation of stochastic integrals and variable-step-size implementations based on various types of control.