MA/CSC 580:  Numerical Analysis I

http://www2.ncsu.edu/eos/info/math/ma580_info/white/ma580hp.htm

This site is being updated during the spring 2000 semester.

 

 

We will examine linear and nonlinear methods that are very important in the numerical solution of differential equations. Both the theory and implementation (via MATLAB) of these methods will be emphasized.  MA/CSC 580 is a three credit course, and MA/CSC 591w is an optional one credit course.  This one credit course will illustrate codes for matrix products, explicit iterations, SOR methods, Gauss elimination, conjugate gradient and GMRES methods.  Here coding will be in F90, MPI and OMP so that the IBM SP multiprocessor can be used.

 

Time and Place:                   MWF 8:05-8:55 in HA 366 for MA/CSC 580

                                               F 9:10-10:00 in HA 269 for MA 591w

 

Instructor:                            R. E. White, Professor of Mathematics, NCSU, 

                                                HA 308, 515-7478, white@math.ncsu.edu

 

Grading:                                Two hour exams (50%) and homework (50%)

 

Prerequisites:                      Matrix Algebra (eg. MA 405) and Advanced Calculus (eg. MA 511)

 

Course Outline:

 

1.0 Introduction

          computational science, floating point numbers

          roundoff and accumulation errors, stability, application to

          heat and mass transfer, computers

 

2.0 Linear, Ax = d, A is nxn and Direct Methods

          Gaussian elimination, row interchanges and pivots, Schur

          complement, symmetric, banded and ill-conditioned matrices

 

3.0 Linear, Ax = d, A is mxn with m ¹ n

          least squares, normal equations, QR factorization methods,

          multiple solutions and equilibrium equations

 

4.0 Eigenvalues, Ax = lx

         approximation, power and Jacobi methods, QR algorithm and

         application to Schrodinger equation

 

5.0 Linear, Ax = d, A is nxn and Iterative Methods via Splittings

          Jacobi, SOR, block versions, convergence theory, P-regular

          splittings, ADI, incomplete factorizations, residual correction

 

6.0 Linear, Ax = d, A is nxn and Iterative Methods via Minimization

          steepest descent, conjugate gradient, preconditioners,

          Krylov and GMRES(m)

 

7.0 Nonlinear, F(x) = 0

          basic methods, variation of mean value theorem,

          Picard method, Newton method and variations


 

Fortran_Codes