Local Optimization Methods

Course guarantor:

Jan Chleboun

Lecturer:

Jan Chleboun

Tutor:

Supervisor:

Department of Mathematics

Synopsis:

The goal is to make students familiar with common methods for the minimization of functions of one or several real variables. Unconstrained as well as constrained minimization are considered. By using software tools (Matlab, SciLab, Octave, Python, etc.), course participants are expected to present a solution to a minimization problem motivated by the subject of their research.

Topics:

Minimization of functions of one real variable.

Unconstrained minimization of functions of several real variables. Conditions for local optimality. Conjugate gradient method, quasi-Newton methods.

Constrained minimization of functions of several real variables. Lagrange multipliers. Conditions for local optimality. Penalty method, active set method, gradient projection method, SQP method (Sequential Quadratic Programming).

Introduction to linear programming, simplex method.

Requirements:

Syllabus of lectures:

Syllabus of tutorials:

Study Objective:

Study materials:

Note:

Time-table for winter semester 2024/2025:

Time-table is not available yet

Time-table for summer semester 2024/2025:

Time-table is not available yet

The course is a part of the following study plans: