# Numerical Analysis II (MATH522) Course Detail

Course Name Course Code Season Lecture Hours Application Hours Lab Hours Credit ECTS
Numerical Analysis II MATH522 3 0 0 3 5
Pre-requisite Course(s)
Consent of the department
Course Language English N/A Ph.D. Face To Face Lecture, Discussion, Question and Answer, Problem Solving. Assoc. Prof. Dr. İnci Erhan This graduate level course is designed to give math students the expertise necessary to understand, construct and use computational methods for the numerical solution of certain problems such as root finding, interpolation, approximation and integration. The emphasis is on numerical methods for solving nonlinear equations and systems, interpolation and approximation, numerical differentiation and integration as well as the error analysis and the criteria for choosing the best algorithm for the problem under consideration. The students who succeeded in this course; Understand the theoretical and practical aspects of the construction and implementation of the numerical methods Establish the advantages, disadvantages and limitations of the numerical methods and select the algorithms that converge to solutions in the most effective way Construct and apply iterative methods for the approximate solution of nonlinear equations and systems. Choose a numerical integration or differentiation method suitable for the problem under consideration and write an interpolation polynomial whenever necessary. Analyze the error and establish the conditions for convergence related to these methods Implement the methods and/or algorithms as computer code and use them to solve applied problems Discuss the numerical methods and/or algorithms with respect to stability, applicability, reliability, conditioning, accuracy, computational complexity and efficiency Iterative methods for nonlinear equations and nonlinear systems, interpolation and approximation: polynomial trigonometric, spline interpolation; least squares and minimax approximations; numerical differentiation and integration: Newton-Cotes, Gauss, Romberg methods, extrapolation, error analysis.

### Weekly Subjects and Releated Preparation Studies

Week Subjects Preparation
1 Iterative methods for nonlinear equation and systems: Newton’s method, Secant Method K. Atkinson- Sec. 2.1, 2.2 ,2.3 R. Kress- Sec. 6.2
2 Iterative methods for nonlinear equation and systems: Regula Falsi, Zeros of polynomials K.Atkinson- Sec. 2.9 R. Kress- Sec. 6.3
3 Interpolation: Lagrange and Newton interpolating polynomials K.Atkinson- Sec. 3.1, 3.2 R. Kress- Sec.8.1
4 Interpolation: Hermite interpolating polynomial, Spline interpolation K. Atkinson- Sec. 3.6,3.7 R. Kress- Sec. 8.3
5 Interpolation: Fourier series, trigonometric interpolation K. Atkinson-Sec. 3.8 R. Kress- Sec. 8.2
6 Approximation: Least squres approximation K. Atkinson- Sec. 4.1,4.3
7 Approximation: Minimax approximation K. Atkinson- Sec. 4.2
8 Numerical differentiation K.Atkinson- Sec. 5.7
9 Midterm Exam
10 Numerical differentiation: error analysis K. Atkinson- Sec. 5.7
11 Numerical integration: Newton-Cotes formulae K. Atkinson- Sec. 5.2 R. Kress- Sec. 9.1
12 Numerical integration: Gaussian quadrature K. Atkinson-Sec. 5.3 R. Kress- Sec. 9.3
13 Numerical integration: Romberg integration R. Kress-Sec. 9.5
14 Numerical integration: Error analysis K. Atkinson- Sec. 5.4 R. Kress- Sec. 9.2
15 Extrapolation methods: Richardson extrapolation, Other references
16 Final Exam

### Sources

Course Book 1. R. Kress, “Numerical Analysis: v. 181 (Graduate Texts in Mathematics)”, Kindle Edition, Springer, 1998. 3. K. E. Atkinson, “An Introduction to Numerical Analysis”, 2nd edition, John Wiley and Sons, 1989 4. J. Stoer, R. Bulirsch, “Introduction to Numerical Analysis”, 3rd edition 5. R. L. Burden, R.J. Faires, “Numerical Analysis”, 9th edition, Brooks/ Cole, 2011.

### Evaluation System

Requirements Number Percentage of Grade
Attendance/Participation - -
Laboratory - -
Application - -
Field Work - -
Special Course Internship - -
Quizzes/Studio Critics - -
Homework Assignments 5 30
Presentation - -
Project - -
Report - -
Seminar - -
Midterms Exams/Midterms Jury 1 30
Final Exam/Final Jury 1 40
Toplam 7 100
 Percentage of Semester Work 60 40 100

### Course Category

Core Courses X

### The Relation Between Course Learning Competencies and Program Qualifications

# Program Qualifications / Competencies Level of Contribution
1 2 3 4 5

Activities Number Duration (Hours) Total Workload
Course Hours (Including Exam Week: 16 x Total Hours)
Laboratory
Application
Special Course Internship
Field Work
Study Hours Out of Class 14 3 42
Presentation/Seminar Prepration
Project
Report
Homework Assignments 5 3 15
Quizzes/Studio Critics
Prepration of Midterm Exams/Midterm Jury 1 10 10
Prepration of Final Exams/Final Jury 1 10 10