Hiroshima University Syllabus

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Academic Year School/Graduate School Lecture Code 2022Year School of Informatics and Data Science KA117001 Specialized Education 数値計算 スウチケイサン Numerical Computation OKAMURA HIROYUKI オカムラ　ヒロユキ Higashi-Hiroshima 2nd-Year,  Second Semester,  4Term (4T) Fri5-8：ENG 218 Lecture Lecture & practice 2.0 B : Japanese／English 3 : Undergraduate High-Intermediate 25 : Science and Technology 02 : Information Science Second-year students Matrix-vector computation, optimization and simulation Informatics and Data Science Program（Comprehensive Abilities）・I2. Ability to provide the most appropriate system solution to a cross-sectional problem in the diversified and complicated information society based on the many forms of cutting edge information technology. This lecture provides the fundamental knowledge and algorithms for numerical analysis such as linear equation, eigenvalue problem, non-linear equation, optimization and Monte-Carlo simulation. Also the students practice the implementation of these algorithms through a programming language. lesson1: Floating-point expression and numerical errorslesson2: Floating-point expression and numerical errorslesson3: Programming, Complexitylesson4: Programming, Complexitylesson5: Linear equationslesson6: Linear equationslesson7: Eigenvalue and eigenvector problems, Sparse matrixlesson8: Eigenvalue and eigenvector problems, Sparse matrixlesson9: Non-linear equationslesson10: Non-linear equationslesson11: Non-linear optimizationlesson12: Non-linear optimizationlesson13: Monte-Carlo simulationlesson14: Monte-Carlo simulationlesson15: Other topics (numerical integration, etc.)Reports should be submitted every week. Basic (minimal necessary) materials are distributed as handouts or electronic files through Bb9. Google Classroom, Google Colaboratory Lessons 1, 2:Understand the expression of integers and real values in computers, and understand numerical errorsLessons 3, 4:Learn how to write numerical computation programming, and understand computational complexity.Lessons 5, 6:Learn the programs for basic linear computation (vector and matrix), and the algorithms to solve linear equations.Lessons 7, 8:Learn the concepts of eigenvalues ​​and eigenvectors in linear algebra, and learn the data structure of sparse matrix.Lessons 9, 10;Learn the algorithms to solve non-linear equations.Lessons 11, 12:Learn the algorithm to solve non-linear optimization problems.Lessons 13, 14:Learn the concept of pseudo-random numbers and Monte-Carlo simulation The score will be judged as the learning achievement on the knowledge of numerical computation. A pass grade is 60 points or more. It is encouraged that students actively ask questions to the teacher and assistants. Please fill in the class improvement questionnaire which is carried out on all classes.Instructors will reflect on your feedback and utilize the information for improving their teaching.
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