Optimization
Course type: elective
Lecturer: Sergio Cabello, Ph.D., Associate Professor
| Study programme and level | Study field | Academic year | Semester |
|---|---|---|---|
| Applied statistics, second level | All modules | 1st or 2nd | 1st or 2nd |
For the timeline see Curriculum .
Prerequisites:
- Regular inscription.
Content (Syllabus outline):
Convex sets and functions, convex programming. Lagrange duality, dual problem, weak and strong duality. Slater's condition, the Karush-Kuhn-Tucker theorem.
Linearly constrained optimization problems, quadratic and semidefinite programming with generalizations. Numerical procedures, penalty functions. Integer programming.
A short overview of software tools for solving optimization problems.
Objectives and competences:
Students encounter the fundamental types of problems in mathematical programming, with emphasis on the convex ones. They get to know the basic mathematical tools for tackling these problems, using appropriate software packages for solving them in practice.
Intended learning outcomes:
Students are able to model various important applied problems accurately. They are familiar with the basic techniques and software tools that can be used to solve the resulting optimization problems efficiently.
