- Code
- CMP 120
- Name
- Numerical Analysis
- Semester
- 2
- Lecture hours
- 3.00
- Seminar hours
- 1.00
- Laborator hours
- 0.00
- Credits
- 3.50
- ECTS
- 5.00
- Description
-
Binary Numbers. Error analysis. Solving systems of linear equations: Gaussian Elimination, modification of Gaussian Elimination and L-U transformation. Solving non-linear equations and systems: Bisection, Newton, Secant methods and fixed point iteration. Interpolation : Lagrange approximation, Newton's polynomials and approximation of polynomials. Curve matching. Numerical differentiations; numerical integrations. Numerical optimizations. Numerical solutions of initial value and peak value problems.: Euler, Heun, Taylor, Runge-Kutta methods.
- Objectives
-
Binary Numbers. Error analysis. Solving systems of linear equations: Gaussian Elimination, modification of Gaussian Elimination and L-U transformation. Solving non-linear equations and systems: Bisection, Newton, Secant methods and fixed point iteration. Interpolation : Lagrange approximation, Newton's polynomials and approximation of polynomials. Curve matching. Numerical differentiations; numerical integrations. Numerical optimizations. Numerical solutions of initial value and peak value problems.: Euler, Heun, Taylor, Runge-Kutta methods.
- Java
- Tema
- 1
- Binary Numbers
- 2
- Error Analysis
- 3
- Solving equations x = g(x). Bracketing methods, Newton's method, Secant method, and Fixed-point iteration methods
- 4
- Aitken's process and Steffensen's and Muller's methods
- 5
- Iteration for non-linear systems
- 6
- Iteration for non-linear systems
- 7
- Newton's method for non-linear systems
- 8
- Midterm Exam
- 9
- Solving non-linear equation systems. Gaussian elimination and L-U decomposition
- 10
- Solving linear equation systems. Modifications of the Gaussian elimination method
- 11
- Visualization of Matrices
- 12
- Newton Polynomials and Polynomial Approximation
- 13
- Numerical Integration. Trapezoidal and Simpson's methods
- 14
- Numerical Differentiation and Integration. Euler's Method
- 15
- Numerical Optimization
- 16
- Final Exam
- 1
- Understanding the difference between solving problems by hand and using a computer
- 2
- Understanding the solutions of numerical methods and having a clear structure of an algorithm
- Quantity Percentage Total percent
- Midterms
- 1 30% 30%
- Quizzes
- 0 0% 0%
- Projects
- 0 0% 0%
- Term projects
- 0 0% 0%
- Laboratories
- 0 0% 0%
- Class participation
- 1 20% 20%
- Total term evaluation percent
- 50%
- Final exam percent
- 50%
- Total percent
- 100%
- Quantity Duration (hours) Total (hours)
- Course duration (including exam weeks)
- 16 4 64
- Off class study hours
- 14 4 56
- Duties
- 0 0 0
- Midterms
- 1 5 5
- Final exam
- 1 0 0
- Other
- 0 0 0
- Total workLoad
- 125
- Total workload / 25 (hours)
- 5.00
- ECTS
- 5.00