State University of New York at Oswego

  1. COURSE NUMBER AND CREDIT

    CSC/MAT 320 - 3 Semester Hours

  2. COURSE TITLE

    Numerical Analysis I

  3. COURSE DESCRIPTION

    The use of numerical methods to solve mathematical problems such as the solutions of equations, interpolation, differentiation, and integration. Computer proficiency is gained by programming the solutions, studying algorithm efficiency, error propagation.

  4. PREREQUISITES

    MAT 220 and MAT 230 and CSC 212

  5. COURSE JUSTIFICATION

    The subject of numerical analysis deals with computational methods to approximate, in an efficient manner, the solutions to mathematical problems that do not have a an analytic solution. Such mathematical problems occur in physical problems. As such, numerical analysis is required in the science and engineering disciplines. Numerical models are also used to solve economics problems.

  6. COURSE OBJECTIVES

    Upon successful completion of this course, students will be able to:

    1. Recognize types of problems and identify numerical methods that are used to solve the problems.
    2. Efficiently program solutions to problems.

  7. COURSE OUTLINE

    1. Solution of equations in one variable
      1. bisection method
      2. Newton's method
      3. fixed-point iteration
      4. error analysis
    2. interpolation and polynomial approximation
      1. Taylor polynomials
      2. interpolation and the Lagrange polynomial
      3. iterated interpolation
      4. cubic spline interpolation
      5. isoparametric interpolation
    3. numerical differentiation and integration
      1. adaptive quadrature methods
      2. Gaussian quadrature
    4. initial-value problems for ordinary differential equations
      1. Euler's method
      2. Runge-Kutta methods
      3. multistep and variable step-size multistep methods
    5. direct methods for solving linear systems
      1. Gaussian elimination and backward substitution
      2. pivoting strategies
      3. special types of matrices
    6. iterative techniques in matrix algebra
      1. norms of vectors and matrices
      2. Eigenvalues and Eigenvectors
      3. iterative techniques
    7. approximation theory
      1. discrete least-squares approximation
      2. Chebyshev polynomials
      3. Fast Fourier Transforms

  8. METHODS OF INSTRUCTION

    1. lecture
    2. discussion
    3. homework assignments
    4. programming assignments
    5. examination

  9. COURSE REQUIREMENTS

    1. Readings from the textbook.
    2. Design and implementation of programs.

  10. MEANS OF EVALUATION

    1. programs
    2. examinations

  11. RESOURCES

    No additional resources are required.

  12. BIBLIOGRAPHY

    R. L. Burden and J. D. Faires. Numerical Analysis (7ed). Brooks Cole, Pacific Grove, 2000.

    S. C. Chapra and R. P. Canale. Numerical Methods for Engineers (4ed). McGraw-Hill Science/Engineering/Math, New York, 2001.

    E. W. Cheney and D. R. Kincaid. Numerical Mathematics and Computing (4ed). Brooks Cole, Pacific Grove, 1999.

    R. Hamming. Numerical Methods for Scientists and Engineers (2ed). Dover Publications, Mineola, New York, 1987.

    W. H. Press (ed), S.A. Teukolsky (ed), W. T. Vetterling, and B. P. Flannery (ed). Numerical Recipes in C(++): The Art of Scientific Computing (2ed). Cambridge University Press, Cambridge, 2002.

    D. Yang. C++ and Object-Oriented Numeric Computing for Scientists and Engineers. Springer Verlag, New York, 2000.

  13. SIGNATURES


  14. Elaine Wenderholm, Computer Science Curriculum Committee Chair Date

    Rameen Mohammadi, Computer Science Department Chair Date

    Undergraduate Curriculum Committee Chair Date


Document:
URL:
Last Update: