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About this document
- Pages
- 12 pages
- Price
- $12 one-time, yours to keep
- Membership
- Included in every plan
- Format
- Subject
- Computer Science, Mathematics
- Module
- INT1264
This document is a course outline for Numerical Analysis, module INT1264. It covers the topics, methods and skills that students will learn, and the teaching methods. It is for students of the Computer Science course.
What's inside
- Course outline and goals
- Topics covered in the course
- Teaching methods
- Skills students will learn
- Case studies
Who it is for: For students of the Numerical Analysis module INT1264. (Undergraduate)
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Summary
Numerical analysis is a branch of mathematics and computer science. Students learn methods for approximating solutions to complex mathematical problems.
Types of errors
Errors in numerical analysis come from different sources. Errors include absolute and relative errors. Students learn how to approximate numbers and calculate errors in approximations.
Linear algebra methods
Linear algebra has several methods for solving systems of linear equations. Methods include matrix operations and determinants. Students practice these methods with sample programs.
Interpolation and regression
Interpolation and regression help estimate values between known data points. Students compare Lagrange and Newton interpolation methods and learn about least squares regression.
Nonlinear equations
Nonlinear equations are common in real-world problems. Students learn methods to approximate solutions. Methods include bisection, secant, Newton-Raphson, and simple iteration.
Key terms
- Absolute error
- The difference between an approximate value and its true value.
- Relative error
- The absolute error divided by the true value.
- Interpolation
- A method for estimating values between known data points.
- Regression
- A statistical method for estimating relationships between variables.
- Least squares
- A regression method that minimizes the sum of the squares of the errors.
- Newton-Raphson
- An iterative method for finding successively better approximations to the roots of a real-valued function.
Questions this document helps with
- What methods can calculate approximate errors?
- Which matrix operations solve linear equations?
- How do interpolation and regression estimate unknown values?
Summary written with AI from the document's text.
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