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Numerical Analysis 2

MTH3502
3 hours English

Numerical Analysis 2

Numerical Analysis2
1 Introduction: / Review of Numerical Analysis 1
2 Solving Linear Systems: / Direct Methods; Gaussian elimination, Partial pivoting, LU factorization. / Iterative Methods; Jacobi method, Gauss-Seidel method, Gauss-Seidel with over-relaxation method
3 Approximating Eigenvalues: / Linear algebra and eigenvalues, The power algorithm, The inverse power algorithm, The Householder transformation, The QR algorithm
4 Approximation Theory: / Discrete least squares approximation, Orthogonal polynomials, Chebyshev polynomials, Economization of power series, Rational function approximation, Trigonometric polynomial approximation
5 Revision
1.1 Mapped to: K1, K2

Develop knowledge and understanding on and show familiarity with various concepts and iterative techniques related to the solution approximation of various linear algebraic problems.

Teaching Strategy Lectures, problem-based learning
Assessment Methods Homework, Midterm and final Exams.
2.1 Mapped to: S1, S2, S7

Solve linear systems using Gaussian elimination with partial pivoting and iteratively using Jacobi method and Gauss-Seidel method.

Teaching Strategy Lectures, exercises
Assessment Methods Homework, Midterm and final Exams.
2.2 Mapped to: S4, S7

Develop an algorithm and a computer program for a numerical method.

Teaching Strategy Lectures, exercises
Assessment Methods Homework, Midterm and final Exams.
2.3 Mapped to: S3, S6, S7

Use iterative methods to solve a linear algebraic system of equations in n variables and apply approximation theory techniques to fit data to a function.

Teaching Strategy Lectures, exercises
Assessment Methods Homework, Midterm and final Exams.
3.1 Mapped to: V1, V2

Develop self-learning skills and solve problems both individually and in collaboration with the classmates.

Teaching Strategy Lectures, project report
Assessment Methods Homework and assignments.