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Monday, April 27, 2020 | History

3 edition of computational complexity of differential and integral equations found in the catalog.

computational complexity of differential and integral equations

Arthur G. Werschulz

computational complexity of differential and integral equations

an information-based approach

by Arthur G. Werschulz

  • 79 Want to read
  • 8 Currently reading

Published by Oxford University Press in Oxford [England], New York .
Written in English

    Subjects:
  • Differential equations -- Numerical solutions.,
  • Integral equations -- Numerical solutions.,
  • Computational complexity.

  • Edition Notes

    Includes bibliographical references (p. [314]-322) and indexes.

    StatementArthur G. Werschulz.
    SeriesOxford mathematical monographs, Oxford science publications
    Classifications
    LC ClassificationsQA372 .W47 1991
    The Physical Object
    Paginationix, 331 p. ;
    Number of Pages331
    ID Numbers
    Open LibraryOL1538783M
    ISBN 100198535899
    LC Control Number91017226

    Algebraic geometry is a branch of mathematics, classically studying zeros of multivariate algebraic geometry is based on the use of abstract algebraic techniques, mainly from commutative algebra, for solving geometrical problems about these sets of zeros.. The fundamental objects of study in algebraic geometry are algebraic varieties, which are . Information-based complexity (IBC) is the branch of computational complexity which studies problems for which the information is partial, contaminated, and priced. The Computational Complexity of Differential and Integral Equations: An Information-Based Approach, Oxford University Press, New York,   It covers all major topics in computational mathematics with a wide range of carefully selected numerical algorithms, ranging from the root-finding algorithms, numerical integration, numerical methods of partial differential equations, finite element methods, optimization algorithms, stochastic models, to nonlinear curve-fitting and swarm. Publication Topics electromagnetic wave scattering,integral equations,Green's function methods,computational electromagnetics,time-domain analysis,computational complexity,electric field integral equations,reduced order systems,image reconstruction,iterative methods,method of moments,graphics processing units,inverse .


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computational complexity of differential and integral equations by Arthur G. Werschulz Download PDF EPUB FB2

In this book, the theory of the complexity of the solution to differential and integral equations is developed. The relationship between the worst case setting and other (sometimes more tractable) related settings, such as the average case, probabilistic, asymptotic, and randomized settings, is also by: The computational complexity of differential and integral equations: an information-based approach.

Book Description. This two volume introduction to the computational solution of differential equations uses a unified approach organized around the adaptive finite element method. It presents a synthesis of mathematical modeling, analysis, and by: Integral equations form an important class of problems, arising frequently in engineering, and in mathematical and scientific analysis.

This textbook provides a readable account of techniques for their numerical : L. Delves, J. Mohamed. The Computational Complexity of Differential and Integral Equations: An Information-Based Approach (Arthur G. Werschulz)Cited by: 1. On the Computational Complexity of Ordinary Differential Equations* KER-I Ko Department of Computer Science, University of Houston, Houston, Texas The computational complexity of the solution y of the differential equation y'(x)=f(x, y(x)), with the initial value y(0)=0, relative to the computational complexity of the function f is.

Computational Complexity of Ordinary Differential Equations Akitoshi KAWAMURA University of Tokyo Ninth International Conference on Computability and Complexity in Analysis Cambridge, UK J 1 / Computational Differential Equation.

A 'read' is counted each time someone views a publication summary (such as the title, abstract, and list of authors), clicks on a figure, or views or downloads the full-text. This book covers the following topics in applied mathematics: Linear Algebraic Systems, Vector Spaces and Bases, Inner Products and Norms, Minimization and Least Squares Approximation, Orthogonality, Equilibrium, Linearity, Eigenvalues, Linear Dynamical Systems, Iteration of Linear Systems, Boundary Value Problems in One Dimension, Fourier Series, Fourier Analysis, Vibration and Diffusion in One-Dimensional Media, The Laplace Equation, Complex Analysis.

A problem will be said to have polynomial complexity if it requires less than CNd steps (or units of time) to be solved, where C and d are constants (d is the degree). • Especially, it is said to have – linear complexity when #steps ≤ CN – quadratic complexity when #steps ≤ CN2 – quasi-linear complexity when #steps ≤ Cε N1+ε.

Constructive and Computational Methods for Differential and Integral Equations Symposium, Indiana University February 17–20, Search within book. Front Matter. An integral equation method for generalized analytic functions.

Wolfgang L. Wendland. Under the direction of the Institute for Informatics and Telematics in Pisa, Calcolo publishes original contributions on numerical analysis and its applications, and on the theory of computation.

The main focus of the journal is on numerical linear algebra, approximation theory and its applications, numerical solutions of differential and integral equations, computational complexity. A recent result showed that the cost of solving initial value problems (IVP) for ordinary differential equations (ODE) is polynomial in the number of digits of accuracy.

This improves on the classical result of information-based complexity, which predicts exponential cost. The computational complexity of Volterra integral equations of the second kind and of the first kind is investigated. It is proved that if the kernel functions satisfy the Lipschitz condition, then the solutions of Volterra equations of the second kind are polynomial-space by: computational complexity of IVP for ODE Solving Ordinary Differential Equations I, Computational Mathematics, Vol.

8 (Springer, Berlin, ). A.G. Werschulz, The Computational Complexity of Differential and Integral Equations (Oxford Science, Oxford, ). Google by: Lecture Notes on Numerical Analysis of Nonlinear Equations. This book covers the following topics: The Implicit Function Theorem, A Predator-Prey Model, The Gelfand-Bratu Problem, Numerical Continuation, Following Folds, Numerical Treatment of Bifurcations, Examples of Bifurcations, Boundary Value Problems, Orthogonal Collocation, Hopf Bifurcation and.

These operators are computed on the subintervals, respectively, and these computations decrease the storage and computational complexity. Using this technique, the integral and differential equations are converted into the solution of system algebraic equations.

Numerical experiments demonstrate the validity and applicability of this by: equations is a common link between all these recent approaches. Advantages of Discrete Differential Modeling The reader will have most probably understood our bias by now: we believe that the systematic construction, inspired by Exterior Calculus, of differential, yet readily discretizable computational.

Computational Mathematics for Differential Equations by N. Kopchenova, I. Maron. Description: This is a manual on solving problems in computational mathematics. The book is intended primarily for engineering students, but may also prove useful for economics students, for graduate engineers, and for postgraduate students and scientific workers in the applied sciences.

CiteSeerX - Document Details (Isaac Councill, Lee Giles, Pradeep Teregowda): We investigate the cost of solving initial value problems for differential algebraic equations depending on the number of digits of accuracy requested. A recent result showed that the cost of solving initial value problems (IVP) for ordinary differential equations (ODE) is polynomial in the number of digits.

X.-J. Yang, Local fractional partial differential equations with fractal boundary problems,Advances in Computational Mathematics and its Applications,1 () X.-J.

Yang, Expression of generalized Newton iteration method via generalized local fractional Taylor series, Advances in Computer Science and its Applications, 1 () Lucid, self-contained exposition of the theory of ordinary differential equations and integral equations.

Especially detailed treatment of the boundary value problem of second order linear ordinary differential equations. Other topics include Fredholm integral equations, Volterra integral equations, much more.

Bibliography. Information-based complexity is a branch of computational complexity and is formulated as an abstract theory with applications in many areas. Since continuous computational problems are of interest in many fields, information-based complexity has common interests and has greatly benefited from these fields.

We develop a new algorithm to solve the system of integral equations. In this new method no need to use matrix weights. Beacause of it, we reduce computational complexity considerable. Using the new algorithm it is also possible to solve an initial boundary value problem for system of parabolic equations.

To verify the efficiency, the results of computational experiments are Cited by: 1. The key insight is simple: the Lipschitz condition means that the feedback in the differential equation is weak. We define a class of polynomial-space computation tableaux with equally weak feedback, and show that they are still polynomial-space complete.

The same technique also settles Ko’s two later questions on Volterra integral : KawamuraAkitoshi. Numerical & Computational Mathematics on the Academic Oxford University Press website Add Algebraic and Differential Topology of Robust Stability to Cart. Edmond A.

Jonckheere. Hardcover Arithmetic, Proof Theory, and Computational Complexity $ Add Arithmetic, Proof Theory, and Computational Complexity to Cart. Integral equations form an important class of problems, arising frequently in engineering, and in mathematical and scientific analysis.

This textbook provides a readable account of techniques for their numerical solution. The authors devote their attention primarily to efficient techniques using high order approximations, taking particular account of situations where singularities are present.

answer to Ko’s question raised inwe show that an initial value problem given by a polynomial-time computable, Lipschitz continuous function can have a polynomial-space complete solution. The key insight is simple: the Lipschitz condition means that the feedback in the differential equation is weak.

We define a class of polynomial-space computation. Introduction. Fast algorithms for solving differential and integral equations are important for large-scale scientific computing. It usually brings down computational complexity for several orders of magnitude.

This ability makes many difficult scientific and engineering problems become analyzable through computational methods. The solution of the nonhomogeneous partial differential equation is therefore approximated by the discretization of the governing equation using the radial polynomial basis function.

To avoid the singularity, the minimum order of the radial polynomial basis function must be greater than two for the second order partial differential equations. Information-based complexity studies optimal algorithms and computational complexity for the continuous problems which arise in physical science, economics, engineering, and mathematical finance.

IBC has studied such continuous problems as path integration, partial differential equations, systems of ordinary differential equations, nonlinear equations, integral equations. The computational time of solving linear equations or inverting matrices has the same identical power law by using the decomposition.

This puzzles me. I am wondering if the two problems are essentially equivalent in the end. $\endgroup$ – Alberto Montina Dec 8 '15 at Get this from a library. Algebraic and Algorithmic Aspects of Differential and Integral Operators 5th International Meeting, AADIOSHeld at the Applications of Computer Algebra Conference, ACASofia, Bulgaria, June, Selected and Invited Papers.

[Moulay Barkatou; Thomas Cluzeau; Georg Regensburger; Markus Rosenkranz] -- This book. An efficient multigrid finite-differences scheme for solving elliptic Fredholm partial integro-differential equations (PIDE) is discussed. This scheme combines a second-order accurate finite difference discretization of the PIDE problem with a multigrid scheme that includes a fast multilevel integration of the Fredholm operator allowing the fast solution of the PIDE by: 1.

Differential Equations and Computational Ways to Solve Them A vast variety of phenomena that one may wish to model are described in terms of differential equations: algebraic relationships among variables and various orders of their derivatives.

numerically involves the same operations as computing a definite integral by taking the limit of. the solutions of fractional order ordinary differential equations, integral equations and fractional order partial differential equations of physical interest. Number of literatures concerning the application of fractional order differential equations in nonlinear dynamics has been grown rapidly in the recent years [ 2,3,5,12 –14,20 ].

The department’s research activities encompass a wide range of applied mathematics and statistics. These set the tone for the faculty’s individual and collaborative research, the master’s doctoral dissertations of the graduate students, and a.

Abstract: ${\mathcal{ H}}^{2}$ -matrix constitutes a general mathematical framework for efficient computation of both partial-differential-equation (PDE) and integral-equation (IE)-based ng linear-complexity ${\mathcal{ H}}^{2}$ matrix-matrix product (MMP) algorithm lacks explicit accuracy control, while controlling accuracy without compromising linear complexity Author: Miaomiao Ma, Dan Jiao.

Solving differential equations of fractional (i.e., non-integer) order in an accurate, reliable and efficient way is much more difficult than in the standard integer-order case; moreover, the majority of the computational tools do not provide built-in functions for this kind of problem.

In this paper, we review two of the most effective families of numerical methods for fractional-order Cited by:   Elementary Illustrations of the Differential and Integral Calculus, by De Morgan Analytic differential equations by Yulij Ilyashenko, Computational Complexity: Author: Kevin de Asis.

In practice, the problem of optimization of computational algorithms for a concrete problem is of vital interest (see,). The formulation of one problem of optimization (see) is as follows. A differential equation is integrated using the Runge–Kutta method with a variable step.Akitoshi Kawamura.

Lipschitz continuous ordinary differential equations are polynomial-space complete. In Proceedings of the 24th Annual IEEE Conference on Computational Complexity (CCC ), pages –, Paris, France, July Awarded the Ronald V. Book Prize for Best Student Paper. DOI = /CCC (but please refer to the.LECTURES on COMPUTATIONAL NUMERICAL ANALYSIS of PARTIAL DIFFERENTIAL EQUATIONS J.

M. McDonough Departments of Mechanical Engineering and Mathematics.