### 2021　Discrete Optimization

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Graduate major in Mathematical and Computing Science
Instructor(s)
Sumita Hanna  Yamashita Makoto
Course component(s)
Lecture    (ZOOM)
Day/Period(Room No.)
Mon3-4()  Thr3-4()
Group
-
Course number
MCS.M421
Credits
2
2021
Offered quarter
2Q
Syllabus updated
2021/3/19
Lecture notes updated
-
Language used
Japanese
Access Index

### Course description and aims

This course introduces a mathematical structure called matroids. To solve discrete optimization problems efficiently, we need to investigate structure of problems. A matroid, which is an abstraction of the notion of linear independence in vector spaces, is a fundamental structure in discrete optimization theory. This course begins with the definition and characterizations of matroids, and then shows properties of matroids and algorithms to solve discrete optimization problems with a matroid structure. The course also explains related topics and concepts such as the polyhedral aspects of matroids and submodular functions.

The aim of this course is to let students recognize that the matroid structure is essential for solving discrete optimization problem. It is often said that matroids appear in most of efficiently solvable discrete optimization problems. Knowledge on structures behind efficiently solvable problems will help you to solve your problem efficiently as much as possible.

### Student learning outcomes

The goals of this course are the following.
1. Students explain basic properties of matroids and the importance in discrete optimization theory
2. Students recognize basic discrete optimization problems having matroid structures
3. Students explain algorithms for basic discrete optimization problems having matroid strictures

### Keywords

discrete optimization, combinatorial optimization, matroid, submodular function, graph algorithm, polyhedron

### Competencies that will be developed

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

### Class flow

In each class we focus on one topic. Students review the topic by taking quizzes during the class.

### Course schedule/Required learning

Course schedule Required learning
Class 1 Introduction, graph theory basics Grasp overview of this course
Class 2 Axiom and characterizations of matroids
Class 3 Graphs and matroids
Class 4 Operations for matroids
Class 5 Maximum weight independent set problem
Class 6 Matroid intersection
Class 7 Matroid intersection theorem
Class 8 Matroid union
Class 9 Applications of matroids (1)
Class 10 Applications of matroids (2)
Class 11 Integer polyhedra
Class 12 Independent set polytope
Class 13 Polymatroids
Class 14 Submodular functions Final report

### 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)

We do not assign textbooks. We will use course materials that are based on the following books.

### Reference books, course materials, etc.

Alexander Schrijver: Combinatorial Optimization - Polyhedra and Efficiency, Springer, 2003.

### Assessment criteria and methods

Students will be assessed by reports and quizzes.

### Related courses

• MCS.T322 ： Combinatorial Algorithms

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

Undergraduate level of linear algebra and linear programming is required. Basic knowledge on graph theory is desirable.