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Numerical Algorithms and Tensor Learning Laboratory
Numerical Algorithms and Tensor Learning LaboratoryDr. Ratikanta Behera
Dr. Ratikanta BeheraTeaching
- Introduction & Motivation (Types of error in numerical computation)
- Multivariable calculus and its applications
- Solutions of Equations of One Variable
- Interpolation and Polynomial Approximation
- Approximation Theory
- Numerical Integration and Differentiation
- Initial-Value Problems for ODEs
- Solutions of Nonlinear Systems of Equations
- Boundary-Value Problems for ODEs
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Primary Reference:
- 1) Richard L. Burden and J. Douglas Faires, Numerical Analysis (9th edition).
- 2) Kendall Atkinson, and Weimin Han, Elementary Numerical Analysis (Third Edition).
- 3) John H. Mathews & Kurtis D. Fink, Numerical Methods Using MATLAB, Prentice Hall.
- a) Project/Homework/Quizzes - 50%
- b) Midterm Exam - 20%
- c) Final exam - 30%
For more details Please see the following links
Outline of the Course:
Numerical methods are routinely used in all disciplines of science and engineering, which serve as the approximate solutions to real-time problems. This course will cover the formulation/mathematics behind the contemporary numerical methods, which later will be applied to solving the example real world problems. The emphasis of the course will be more on the deep understanding of these numerical methods, with homework geared towards application and analysis of this. Midterm and final exam will be geared towards testing your understanding of advantages/limitations of numerical methods.
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