MATH-261 / 5 credits

Teacher: Marcus Adam Wade

Language: English


Summary

This course is an introduction to linear and discrete optimization. Warning: This is a mathematics course! While much of the course will be algorithmic in nature, you will still need to be able to prove theorems.

Content

Keywords

Linear Programming, Algorithms, Complexity, Graphs, Optimization

Learning Prerequisites

Required courses

Linear Algebra

Recommended courses

Discrete Mathematics or Discrete Structures

Important concepts to start the course

The student needs to be comfortable reading and writing formal mathematical proofs.

Learning Outcomes

By the end of the course, the student must be able to:

  • Choose appropriate method for solving basic discrete optimization problem
  • Prove basic theorems in linear optimization
  • Interpret computational results and relate to theory
  • Implement basic algorithms in linear optmization
  • Describe methods for solving linear optimization problems
  • Create correctness and running time proofs of basic algorithms
  • Solve basic linear and discrete optimization problems

Transversal skills

  • Continue to work through difficulties or initial failure to find optimal solutions.
  • Use both general and domain specific IT resources and tools

Teaching methods

Ex cathedra lecture, exercises in the classroom and with a computer

Expected student activities

  • Attendance of lectures and exercises
  • Completion of exercises
  • Solving supplementary programs with the help of a computer

Assessment methods

Written exam during the exam session

Dans le cas de l'art. 3 al. 5 du Règlement de section, l'enseignant décide de la forme de l'examen qu'il communique aux étudiants concernés.

Resources

Bibliography

Dimitris Bertsimas and John N. Tsitsiklis: Introduction to Linear Optimization, Athena Scientific

Ressources en bibliothèque

Notes/Handbook

Lecture notes

In the programs

  • Semester: Spring
  • Exam form: Written (summer session)
  • Subject examined: Discrete optimization
  • Lecture: 2 Hour(s) per week x 14 weeks
  • Exercises: 2 Hour(s) per week x 14 weeks
  • Semester: Spring
  • Exam form: Written (summer session)
  • Subject examined: Discrete optimization
  • Lecture: 2 Hour(s) per week x 14 weeks
  • Exercises: 2 Hour(s) per week x 14 weeks
  • Semester: Spring
  • Exam form: Written (summer session)
  • Subject examined: Discrete optimization
  • Lecture: 2 Hour(s) per week x 14 weeks
  • Exercises: 2 Hour(s) per week x 14 weeks
  • Semester: Spring
  • Exam form: Written (summer session)
  • Subject examined: Discrete optimization
  • Lecture: 2 Hour(s) per week x 14 weeks
  • Exercises: 2 Hour(s) per week x 14 weeks
  • Semester: Spring
  • Exam form: Written (summer session)
  • Subject examined: Discrete optimization
  • Lecture: 2 Hour(s) per week x 14 weeks
  • Exercises: 2 Hour(s) per week x 14 weeks
  • Semester: Spring
  • Exam form: Written (summer session)
  • Subject examined: Discrete optimization
  • Lecture: 2 Hour(s) per week x 14 weeks
  • Exercises: 2 Hour(s) per week x 14 weeks

Reference week

 MoTuWeThFr
8-9     
9-10     
10-11     
11-12     
12-13     
13-14     
14-15     
15-16     
16-17     
17-18     
18-19     
19-20     
20-21     
21-22