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
Course Type N/A
Course Level Natural & Applied Sciences Master's Degree
Mode of Delivery Face To Face
Learning and Teaching Strategies Lecture, Discussion, Question and Answer, Problem Solving.
Course Coordinator
Course Lecturer(s)
  • Assoc. Prof. Dr. İnci Erhan
Course Assistants
Course Objectives 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.
Course Learning Outcomes 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
Course Content 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


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
Other Sources 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
Percentage of Final Work 40
Total 100

Course Category

Core Courses
Major Area Courses
Supportive Courses X
Media and Managment Skills Courses
Transferable Skill Courses

The Relation Between Course Learning Competencies and Program Qualifications

# Program Qualifications / Competencies Level of Contribution
1 2 3 4 5
1 Has the ability to apply scientific knowledge gained in the undergraduate education and to expand and extend knowledge in the same or in a different area.
2 Has the ability to obtain, to evaluate, to interpret and to apply information by doing scientific research.
3 Can apply gained knowledge and problem solving abilities in inter-disciplinary research.
4 Has the ability to work independently within research area, to state the problem, to develop solution techniques, to solve the problem, to evaluate the obtained results and to apply them when necessary.
5 Takes responsibility individually and as a team member to improve systematic approaches to produce solutions in unexpected complicated situations related to the area of study.
6 Can develop strategies, implement plans and principles on the area of study and can evaluate obtained results within the framework.
7 Can develop and extend the knowledge in the area and to use them with scientific, social and ethical responsibility.
8 Has the ability to follow recent developments within the area of research, to support research with scientific arguments and data, to communicate the information on the area of expertise in a systematically by means of written report and oral/visual presentation.
9 Has proficiency in English language and has the ability to communicate with colleagues and to follow the innovations in mathematics and related fields.
10 Has software and hardware knowledge in the area of expertise, and has proficient information and communication technology knowledge.
11 Has professional ethical consciousness and responsibility which takes into account the universal and social dimensions in the process of data collection, interpretation, implementation and declaration of results in mathematics and its applications.

ECTS/Workload Table

Activities Number Duration (Hours) Total Workload
Course Hours (Including Exam Week: 16 x Total Hours)
Special Course Internship
Field Work
Study Hours Out of Class 14 3 42
Presentation/Seminar Prepration
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
Total Workload 77