Mathematical Programming
| Numbering Code | U-AGR03 2C144 LJ83 | Year/Term | 2022 ・ First semester | |
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| Number of Credits | 2 | Course Type | Lecture | |
| Target Year | 2nd year students | Target Student | ||
| Language | Japanese | Day/Period | Wed.2 | |
| Instructor name |
MIYASAKA JURO (Graduate School of Agriculture Assistant Professor) OODOI KATSUAKI (Graduate School of Agriculture Assistant Professor) |
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| Outline and Purpose of the Course |
Mathematical programming is a field in operations research and a method that provides a mathematical solution to achieve profit maximization or cost minimization under certain constraining conditions. In the first seven sessions, I will explain data science methods, the application of which is spreading fast of late, and explain the basics of data analysis for optimization and modeling. In the second seven sessions, I will demonstrate how it can be applied to the optimization and streamlining of work using a real-life example from an agricultural site. I will explain mainly the simplex method of linear programming as a concrete example, so that students can acquire basic knowledge of mathematical programming. |
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| Course Goals |
To understand the basics of data science and grasp the basic concepts of data analysis, modeling, and model evaluation. To be able to solve linear programming using the simplex method and apply it to a case in agriculture. |
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| Schedule and Contents |
The lectures are organized as listed below. The first seven lectures are on the basics in data science, and the second seven lectures are on linear programming. 【Basics in data science】（Miyasaka） 1. Basic concepts in data science 2. Modeling 1 3. Modeling 2 4. Overfitting 5. Similarity and neighborhood 6. Evaluation and visualization of the model 7. Bayesian theory 【Linear programming (the simplex method)】(Ohdoi) 8. What is operations research (OR)? (Decision making, definition of OR, what is mathematical programming?, and formulation) 9. What is linear programming? (solution using executable area, executable solution, and optimum solution and diagrams) 10. Round-robin algorithm (endpoints of executable area) 11. Introduction of standard form (slack variable) 12. Use of standard form (determination of executable solution) 13. Ideas behind the simplex method 14. Arriving at a solution using the simplex method ＜＜end-of-semester exam＞＞ 15. Feedback |
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| Evaluation Methods and Policy |
【Assessment method】 Assessment is based on the end-of -semester exam (100 points). Depending on the spread of the novel coronavirus (COVID-19), the exam could be substituted with a report. In principle, students have to attend at least two-thirds of classes in order to sit the exam. Additionally, three late arrivals to class will be counted as one missed class. 【Assessment criteria】 Assessment criteria and policy are drawn from "Assessment criteria and policy" in the current version of the Faculty of Agriculture Student Handbook. |
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| Course Requirements | None | |||
| Study outside of Class (preparation and review) |
【Basics in data science】 Preparation: Using the textbook, read the relevant sections before coming to each lecture. Review: Students are expected to investigate cases that are related to each lecture. You are also expected to try out the data science method on your computer to experience it. 【Linear programming (the simplex method)】 Be sure to review the calculation process discussed in class, and attempt calculations with an example from your life. |
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| Textbooks | Textbooks/References | Foster Provost，Tom Fawcett, Data Science for Business: What You Need to Know about Data Mining and Data-Analytic Thinking, O'Reilly Media, 2013, ISBN:ISBN978-1449361327 （Used in 【Basics in Data Science】） | ||
