This course studies properties and solution methods for fundamental optimization models, which include Linear Programming, Nonlinear Programming, Network Optimization and Combinatorial Optimization Problems.
The technology of operations research is useful to do decision making for various problems in management sciences. Knowledge and ability acquired through this course will help students to solve real optimization problems in the future.
By the end of this course, students will be able to:
・Understand fundamental properties of linear programming and use the simplex method.
・Understand fundamental properties of nonlinear programming and use the steepest descent method and the Newton method.
・Understand fundamental properties of network programming problems and use its solution methods.
・Understand fundamental properties of Knapsack problems and use the branch and bound method.
Linear programming, Nonlinear programming, network optimization, Combinatorial optimization
✔ Specialist skills | Intercultural skills | Communication skills | Critical thinking skills | ✔ Practical and/or problem-solving skills |
In each lecture, lecture materials will be uploaded to T2SCHOLA so that students can read and understand the content.
Before the end of the class, exercises related to the contents of the day's class will be presented, and students are expected to solve them before the next class as a report.
Submission of the report is optional, but points will be added to the final grade depending on how well the report is completed.
A mid-term exam and a final exam will be given to check the level of understanding.
Course schedule | Required learning | |
---|---|---|
Class 1 | intoroduction to operations research and lienar programming | Instructions will be given in each class. |
Class 2 | linear programming | Instructions will be given in each class. |
Class 3 | linear programming | Instructions will be given in each class. |
Class 4 | linear programming | Instructions will be given in each class. |
Class 5 | linear programming | Instructions will be given in each class. |
Class 6 | combinatorial optimization | Instructions will be given in each class. |
Class 7 | mid-term exam | Instructions will be given in each class. |
Class 8 | network optimization | Instructions will be given in each class. |
Class 9 | network optimization | Instructions will be given in each class. |
Class 10 | network optimization | Instructions will be given in each class. |
Class 11 | nonlinear optimization | Instructions will be given in each class. |
Class 12 | nonlinear optimization | Instructions will be given in each class. |
Class 13 | nonlinear optimization | Instructions will be given in each class. |
Class 14 | final exam | Instructions will be given in each class. |
To enhance effective learning, students are encouraged to spend approximately 100 minutes preparing for class and another 100 minutes reviewing class content afterwards (including assignments) for each class.
They should do so by referring to textbooks and other course material.
None required
Course materials will be uploaded on T2SCHOLA.
Students will be assessed on their understanding of linear programming, nonlinear programming, network optimization, and combinatorial optimization, and their ability to apply them to solve problems.
Students' course scores are based on midterm and final exams (80%) and reports (20%).
No prerequisites
The lecture materials of the last year are available at the following web page.
http://www.iee.e.titech.ac.jp/~shioura/teaching/orf22/index.html