Course title
401130402
Numerical Analysis

 HIROSE Sampei Click to show questionnaire result at 2019

NAKAGAWA Takahiro Click to show questionnaire result at 2019
Course description
Numerical analysis is a field to obtain approximate solutions of mathematical problems which are difficult to obtain exact solutions. This field has been developed with the development of computers, and is used daily in various fields.
In this class, students learn basic algorithms in numerical analysis, and calculation using a computer.
Purpose of class
The purpose of this class is to understand the purpose of numerical analysis and to enable students to calculate basic algorithms not only by hand but also by computer.
Goals and objectives
  1. To be able to understand basic facts in numerical analysis such as handling of numerical values in a computer and errors.
  2. To be able to apply basic algorithms for mathematical problems.
  3. To be able to implement basic algorithms in Python.
Language
Japanese
Class schedule

 Class schedule HW assignments (Including preparation and review of the class.) Amount of Time Required
1. Overview of numerical analysis and error / Evaluation of algorithms Read through the handouts, and solve the exercises before class. Review after class. 380minutes
2. Examples of algorithms (variance, function value) / Implementation in Python: Calculating variance Read through the handouts, and solve the exercises before class. Review after class. 380minutes
3. Bisection method and Newton's method / Implementation in Python: Bisection method and Newton's method Read through the handouts, and solve the exercises before class. Review after class. 380minutes
4. Gaussian elimination and LU decomposition method / Implementation in Python: Gaussian elimination and LU decomposition method Read through the handouts, and solve the exercises before class. Review after class. 380minutes
5. Newton–Cotes formulas and composite rules / Implementation in Python: Newton–Cotes formulas and composite rules Read through the handouts, and solve the exercises before class. Review after class. 380minutes
6. Euler method, Heun's method and Runge–Kutta method / Implementation in Python: Euler method, Heun's method and Runge–Kutta method Read through the handouts, and solve the exercises before class. Review after class. 380minutes
7. Examination and its explanation Preparation of examination. 380minutes
Total. - - 2660minutes
Relationship between 'Goals and Objectives' and 'Course Outcomes'

 Examination Reports and little examinations Total.
1. 5% 3% 8%
2. 30% 20% 50%
3. 25% 17% 42%
Total. 60% 40% -
Evaluation method and criteria
Examination(60%), Reports and little examinations(40%)
60% if students can understand and solve exercises in the handouts
Feedback on exams, assignments, etc.
ways of feedback specific contents about "Other"
The Others 状況に応じてフィードバックを行う
Textbooks and reference materials
To be introduced in class as appropriate
Prerequisites
Review Differential and Integral Calculus 1, Linear Algebra 1, Differential and Integral Calculus 2, Mathematical Modeling, and Introduction to Data and Science
Office hours and How to contact professors for questions
  • 30 minutes after class
Regionally-oriented
Non-regionally-oriented course
Development of social and professional independence
  • Course that cultivates an ability for utilizing knowledge
Active-learning course
More than one class is interactive
Course by professor with work experience
Work experience Work experience and relevance to the course content if applicable
N/A N/A
Education related SDGs:the Sustainable Development Goals
  • 9.INDUSTRY, INNOVATION AND INFRASTRUCTURE
Last modified : Sat Sep 09 06:06:03 JST 2023