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Code: | EEC0016 | Acronym: | MNUM |

Keywords | |
---|---|

Classification | Keyword |

OFICIAL | Mathematics |

Active? | Yes |

E-learning page: | http://moodle.fe.up.pt/ |

Responsible unit: | Department of Electrical and Computer Engineering |

Course/CS Responsible: | Master in Electrical and Computers Engineering |

Acronym | No. of Students | Study Plan | Curricular Years | Credits UCN | Credits ECTS | Contact hours | Total Time |
---|---|---|---|---|---|---|---|

MIEEC | 370 | Syllabus (Transition) since 2010/2011 | 2 | - | 6 | 63 | 162 |

Syllabus | 2 | - | 6 | 63 | 162 |

Lectures: | 2,00 |

Recitations: | 2,00 |

Type | Teacher | Classes | Hour |
---|---|---|---|

Lectures | Totals | 2 | 4,00 |

Aníbal Castilho Coimbra de Matos | 2,00 | ||

Carlos Alberto Costa Mendonça e Moura | 2,00 | ||

Recitations | Totals | 12 | 24,00 |

Manuel Joaquim da Silva Oliveira | 6,00 | ||

Joaquim José de Amaral Vieira e Costa | 12,00 | ||

Ana Cristina Costa Aguiar | 2,00 | ||

Carlos Alberto Costa Mendonça e Moura | 4,00 |

Electrical and computer engineers make an increasing use of numerical models and tools to solve problems. A solid knowledge of those tools is required for design engineers.

2- SPECIFIC AIMS

To endow students with skills so that they can rigorously apply numerical techniques to solve engineering problems. Topics covered include: finite precision representation of numbers, iterative methods to solve nonlinear equations, function approximation, numerical methods for linear algebra, numerical integration and numerical solution of ordinary differential equations.

3- PREVIOUS KNOWLEDGE

Basic courses on Calculus.

4- PERCENT DISTRIBUTION

Scientific component:70%

Technological component:30%

5- LEARNING OUTCOMES

Knowledge and Understanding- Understanding the limitations of finite precision, understanding the approximate nature of numerical tools.

Engineering analysis- Analysis of convergence of iterative methods.

Transferable skills- Knowledge of iterative methods to find numerical solutions of equations and systems of equations; knowledge of numerical approximation techniques.

II. Non-linear equations: Direct and iterative methods; Separation of zeros of functions; Methods of successive bisection, false position, simple iterative, Newton and secant; Errors and convergence; Polynomial zeros.

III. Systems of non-linear equations: fixed-point method; Newton’s method; Errors and convergence; Modifications of Newton’s method.

IV. Function approximation: least squares method; Method extension; Approximation in vector spaces.

V. Polynomial interpolation: interpolation polynomial; Divided differences and finite differences; Interpolation error; Double and inverse interpolation; Segmented polynomial interpolation (splines).

VI. Numerical integration: Rules of simple and composite integration; Trapezium rule and Simpson’s rule; Romberg integration; Gaussian quadrature;

VII. Linear equations systems: Gaussian elimination; Pivoting techniques; Iterative methods: Jacobi and Gauss-Sidel; Error and residual of an approximate solution; Relationship between them.

VIII. Integration of differential equations: Euler method; Taylor method; Truncation error; Consistency; Predictor-corrector method; Runge Kutta, Milne and Adams methods.

Burden, Richard L.; Numerical analysis. ISBN: 0-53491-585-X

Conte, S. D.; Elementary numerical analysis. ISBN: 0-07-012447-7

E. Fernandes; Computação Numérica, Universidade do Minho

W. Cheney, R. Kincaid; Numerical Mathematics and Computing, Brooks Cole

Theoretical-practical classes: programming of methods and techniques of Numerical Analysis using “Matlab”; resolution and discussion of exercises.

Physical sciences > Mathematics > Applied mathematics > Numerical analysis

Description | Type | Time (hours) | Weight (%) | End date |
---|---|---|---|---|

Attendance (estimated) | Participação presencial | 56,00 | ||

Exams | Exame | 3,00 | ||

Total: |
- | 0,00 |

Description | Type | Time (hours) | End date |
---|---|---|---|

Study | Estudo autónomo | 78 | |

Preparation for exams | Estudo autónomo | 25 | |

Total: |
103,00 |

- cannot miss more classes than allowed by the rules (practical and theoretical-practical);

- have to reach a minimum grade of 8 (out of 20) in programming assignments and exercises;

Students, who attended this course last year, do not need to attend practical and theoretical-practical classes. Students have to choose at the beginning of the semester and the decision is irreversible.

N = (0.1+0.01xE)xP + (0.9-0.01xE)xE

For P it will only be taken into account the 4 best programming assignments (out of 5) and 4 best problems (out of 5).

Students, who want to keep their continuous assessment grade from last year, their Final Grade (N) will be based on the same formula, being the grade of practical classes substituted by last year continuous assessment grade.

Students can only reach a higher grade than 18 (out of 20) if they attend an oral exam.

- the grade of practical classes will be substituted by a programming assignment or an extra exercise (P);

- final grade will be based on the following formula:

N = (0.1+0.01xE)xP + (0.9-0.01xE)xE.

And the average grade of the two tests.

N = (0.1+0.01xE)xP + (0.9-0.01xE)xE

E- Exam

P- Programming assignment (to be done at the day of the exam)

Students can only reach a higher grade than 18 (out of 20) if they attend an oral exam.

All assessment components are graded from 0 to 20.

The assessment of the practical component is based on the average grade of the programming assignments and problems solved in class. It will be taken into account the 4 best results. If students miss a class, in which it will take place an exercise/assignment, they will earn a 0 in that exercise/assignment.

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Page generated on: 2019-04-26 at 09:52:29

Page generated on: 2019-04-26 at 09:52:29