Course detail
Applied Mathematics for Logistics
FSI-SMO-AAcad. year: 2024/2025
The subject is designed to deepen knowledge of applied mathematics, especially areas often used in logistics such as linear algebra, mathematical analysis, optimization and probability and statistics.
Language of instruction
Number of ECTS credits
Mode of study
Guarantor
Department
Entry knowledge
Knowledge of foundations of the following topics is required:
- differential and integral calculus of one-variable functions
- vector and matrix calculus
- numerical optimisation
- probability
Rules for evaluation and completion of the course
Course-unit credit requirements: Active attendance at the seminars, at least 50% of points in written tests. There is one alternative date to correct these tests.
Form of examination: The exam is written and oral.
The written part takes 100 minutes and contains 6 exercises.
The oral part takes 20 minutes and 2 questions are asked.
At least 50% of the correct results must be obtained from the written part. If less is achieved, then the overall classification is F (failed).
Exercises are evaluated by 3 points each, questions by up to 12 point altogether.
Total classification is given by the sum of points from both parts.
A (excellent): 27 - 30 points
B (very good): 24 - 26 points
C (good): 21 - 23 points
D (satisfactory): 19 - 21 points
E (enough): 15 - 18 points
F (failed): 0 - 14 points
Attendance at lectures is recommended, attendance at seminars is required. The lessons are planned on the basis of a weekly schedule. The way of compensation for an absence is in the competence of the teacher.
Aims
The subject provides a survey of particular fields in applied mathematics necessary for understanding the topics in other subjects. The parts on linear algebra is crucial because it appears in optimisation algorithms as well as in selected methods of statistical multivariate analysis.
Study aids
Prerequisites and corequisites
Basic literature
M. Jukl, Linear algebra lessons. Olomouc: Univerzita Palackého v Olomouci, 2020. (EN)
R. A. Johnson and D. W. Wichern, Applied multivariate statistical analysis, Sixth edition [Pearson modern classic edition]. Upper Saddle River, New Jersey: Pearson, [2019]. (EN)
S. Trávníček, P. Calábek, and J. Švrček, Differential calculus I: (for teacher studies). Olomouc: Palacký University Olomouc, 2021. (EN)
R. Sobot, Engineering Mathematics by Example, Springer Cham, [2022]. (EN)
Recommended reading
Classification of course in study plans
Type of course unit
Lecture
Teacher / Lecturer
Syllabus
Week 1-4: Selected topics in linear algebra, eigenvalues, eigenvectors and subspaces, bilinear and quadratic forms
Week 5-6: Principles of linear optimization, simplex method (focused on specific constrains and methods of solutions)
Week 7-9: Optimization for multivariable functions, local, global and constrained extremes. Foundations of multiple integrals, Fubini theorem, transformations of coordinates
Week 10-13: Cluster analysis, principal component analysis, factor analysis
Exercise
Teacher / Lecturer
Syllabus
In the first exercise we recall elementary notions from differential and integral calculus of one variable functions and foundations of linear algebra. Tutorial examples will be calculated. Further exercises will topically follow the lectures from the previous week.