Course: Optimization in Practice

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Course title Optimization in Practice
Course code KI/APOPT
Organizational form of instruction Seminary
Level of course Bachelor
Year of study not specified
Semester Summer
Number of ECTS credits 1
Language of instruction English
Status of course Compulsory-optional
Form of instruction Face-to-face
Work placements This is not an internship
Recommended optional programme components None
Lecturer(s)
  • Kubera Petr, RNDr. Ph.D.
Course content
1. Linear optimization problem - formulation, application of linear programming (LP) 2. Simplex algorithm 3. Simple implementation of LP solver, convergence problems 4. Existing libraries for LP in Python and C# 5. Integer programming 6. Examples of nonlinear optimization 7. 1D minimization algorithms 8. Gradient and momentum method 9. Neural network (MLP) as optimization problem 10. Constrained optimization, barrier and penalty method 11. Quadratic programming, formulation and existing solvers 12. Support vector machines as quadratic problem

Learning activities and teaching methods
unspecified
Learning outcomes
This course provides a practical introduction to optimization. The course is focused on the introduction to the problem and we emphasize practical problem solution. Basic principles of algorithms used for the solution of the problems are introduced. Students will gain the ability to implement their own algorithms as well as to use existing libraries.

Prerequisites
unspecified

Assessment methods and criteria
unspecified
An individual seminar work focused on model implementation in C# or Python. The seminar work must be approved by teacher.
Recommended literature


Study plans that include the course
Faculty Study plan (Version) Category of Branch/Specialization Recommended year of study Recommended semester
Faculty: Faculty of Science Study plan (Version): Information Sciences (double subject) (A14) Category: Informatics courses - Recommended year of study:-, Recommended semester: -
Faculty: Faculty of Science Study plan (Version): - (A14) Category: Informatics courses - Recommended year of study:-, Recommended semester: -
Faculty: Faculty of Science Study plan (Version): Information Sciences (double subject) (A14) Category: Informatics courses - Recommended year of study:-, Recommended semester: -