2022　Mathematical Models and Computer Science

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Graduate major in Mathematical and Computing Science
Instructor(s)
Yamashita Makoto  Amaya Kenji  Kurabayashi Daisuke  Sumita Hanna
Class Format
Lecture    (Face-to-face)
Media-enhanced courses
Day/Period(Room No.)
Tue5-6(W934)  Fri5-6(W934)
Group
-
Course number
MCS.T506
Credits
2
2022
Offered quarter
4Q
Syllabus updated
2022/9/5
Lecture notes updated
-
Language used
Japanese
Access Index

Course description and aims

This course gives an overview of numerical methods for mathematical optimization problems and introduces some applications of optimization to solve problems arising from engineering. The numerical methods covered in this course include primal-dual interior-point method for linear programming problems, and various methods for nonlinear optimization problems and constrained optimization problems. The topic also includes combinatorial optimization and its numerical methods. As the applications to engineering, the course introduces mechanical measurements and optimization methods that imitate nature.

The rapid developments of computers have brought significant changes to mathematical methodology. To use this progress, new mathematical computing technology has increased in importance. One of such mathematical technology is numerical methods for mathematical optimization problems. This course introduces various mathematical methods. The knowledge of multiple methods will be an advantage to tackle practical problems. In addition, the introduction of applications in engineering will give a chance to understand the rapid developments of computers and optimization methods more deeply.

Student learning outcomes

At the end of this course, students will be able to:

(1) Formulate various problems (including applications in engineering) using mathematical optimization models.
(2) Explain the framework of primal-dual interior-point method for linear programming problems.
(3) Apply numerical methods to solve nonlinear optimization problems.
(4) Explain the relation in conic optimization problems.
(5) Explain numerical methods for combinatorial optimization problems.

Keywords

Mathematical optimization, primal-dual interior-point method, numerical optimization method, optimality condition, second-order cone programming, semidefinite programming, optimization in engineering, combinatorial optimization, meta-heuristics, branch-and-bound method, semidefnite programming relaxation

Competencies that will be developed

 ✔ Specialist skills Intercultural skills Communication skills Critical thinking skills ✔ Practical and/or problem-solving skills

Class flow

This course gives an overview of various types of optimization methods.
At the end of each class, exercise problems are given. Students must solve the exercise problems to review the class.

Course schedule/Required learning

Course schedule Required learning
Class 1 Overview of mathematical optimization, simplex method for linear programming Explain main properties of mathematical optimization models Apply the computation steps of the simplex method for small linear programming problems
Class 2 Brand-and-bound method Explain the framework of the branch-and-bound method for integer programming.
Class 3 Combinatorial optimization problems, meta-heuristics Compare meta-heuristic methods.
Class 4 Steepest descent method, Newton method Compare the merits and the demerits of the steepest decent method, Newton method.
Class 5 Quasi Newton method, trust-region method Explain the frameworks of quasi Newton method and trust-region method.
Class 6 Theoretical aspects of constrained nonlinear optimization problems Explain relations between optimal solutions and optimality conditions.
Class 7 Augmented Lagrange method, primal-dual interior-point method Explain the frameworks of augmented Lagrange method and primal-dual interior-point method.
Class 8 Semidefinite programming Formulate optimization problems into semidefinite programming.
Class 9 Second-order cone programming Formulate optimization problems into second-order cone programming.
Class 10 Optimization in mechanical measurement (via Zoom) Explain what kind of problems in mechanical measurements are solved by optimization.
Class 11 Optimization methods that imitate nature (via Zoom) Find and explain optimization problems from daily life.
Class 12 Efficiency improvements on optimization methods (via Zoom) Understand various improvements on optimization methods.
Class 13 Semidefinite programming relaxation Formulate semidefinite programming problems based on semidefinite programming relaxation
Class 14 Recent topics Review the numerical methods covered for better understanding.

Out-of-Class Study Time (Preparation and Review)

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.

Textbook(s)

None required. Parts of the course materials are based on the reference books below.

Reference books, course materials, etc.

Reference books are follows.
D. P. Bertsekas, "Nonlinear Programming", Athena Scientific, 2003.
V. Chvatal, "Linear programming", Freedman, 1983
R. Horst, P. M. Pardalos, N. V. Thoai, "Introduction to Global optimization", Klewer Academic, 2000
D. Z. Zu, P. M. Pardalos, "Handbook of Combinatorial Optimization", Klewer Academic, 1998

Other reference books are listed in the course materials.

Assessment criteria and methods

Students will be assessed on their understanding on mathematical formulation based on mathematical optimization problems and algorithmic framework of optimization methods.
Students' course scores are based on reports.

Related courses

• MCS.T302 ： Mathematical Optimization
• MCS.T402 ： Mathematical Optimization: Theory and Algorithms
• ICT.M310 ： Mathematical Programming
• IEE.A430 ： Numerical Optimization

Prerequisites (i.e., required knowledge, skills, courses, etc.)

The following knowledge are required.
* The simplex method for linear programming problems
* Linear algebra (in particular, positive semidefinite matrices)

Other

The 10th, 11th, 12th lectures will be given via Zoom.