SubjectsSubjects(version: 945)
Course, academic year 2023/2024
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Optimisation Theory - NMSA403
Title: Teorie optimalizace
Guaranteed by: Department of Probability and Mathematical Statistics (32-KPMS)
Faculty: Faculty of Mathematics and Physics
Actual: from 2022
Semester: winter
E-Credits: 5
Hours per week, examination: winter s.:2/2, C+Ex [HT]
Capacity: unlimited
Min. number of students: unlimited
4EU+: no
Virtual mobility / capacity: no
State of the course: not taught
Language: Czech, English
Teaching methods: full-time
Teaching methods: full-time
Guarantor: doc. RNDr. Petr Lachout, CSc.
Class: M Mgr. FPM
M Mgr. FPM > Povinně volitelné
M Mgr. PMSE
M Mgr. PMSE > Povinné
Classification: Mathematics > Optimization
Incompatibility : NMSA413
Interchangeability : NMSA413
Is incompatible with: NMSA413
Is pre-requisite for: NMEK532
Is interchangeable with: NMSA413, NEKN012, NEKN035
Annotation -
Last update: RNDr. Jitka Zichová, Dr. (02.05.2018)
Optimization in economy and statistics, convex analysis, introduction to non-linear programming, theory of linear programming with respect to convex analysis and general optimization. Supposed knowledge: Mathematical analysis (functions with several arguments, constraint extrema problems).
Aim of the course -
Last update: T_KPMS (14.05.2013)

To give explanation and theoretical background for standard optimization procedures. Students will lern necessary theory and practice their knowladge on numerical examples.

Course completion requirements -
Last update: doc. RNDr. Martin Branda, Ph.D. (01.10.2021)

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Course finalization

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The course is finalized by a credit from the exercises class and exam.

The exercises class credit is necessary to sign up for the exam.

Conditions for receiving of a credit from exercises class are:

  • Two written tests (70% points from each test are necessary).
  • Each test can be repeated only once at the end of the semester.
  • Attempt to receive a credit from exercises class cannot be repeated.

Literature - Czech
Last update: T_KPMS (20.04.2015)

Bazaraa, M.S.; Sherali, H.D.; Shetty, C.M.: Nonlinear programming: theory and algorithms. Wiley, New York, 1993.

Bertsekas, D.P.: Nonlinear programming. Athena Scientific, Belmont, 1999.

Dantzig, G.B.; Thapa, M.N.: Linear programming. 1,2. Springer, New York, 1997.

Luenberger, D.G.; Ye, Y.: Linear and Nonlinear Programming. 3rd edition, Springer, New York, 2008.

Plesník, J.; Dupačová, J.; Vlach, M.: Lineárne programovanie. Alfa, Bratislava, 1990.

Rockafellar, T.: Convex Analysis. Springer-Verlag, Berlin, 1975.

Rockafellar, T.; Wets, R. J.-B.: Variational Analysis. Springer-Verlag, Berlin, 1998.

Teaching methods -
Last update: T_KPMS (14.05.2013)

Lecture + exercises.

Requirements to the exam -
Last update: doc. RNDr. Martin Branda, Ph.D. (01.10.2021)

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Requirements to exam

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The exam is contained from a written part and an oral part. Written part is foregoing to oral part.

If written part is not fulfilled, whole exam is marked as non-satisfactory, and oral part is not treated.

Mark from the examination is determined considering results from both written and oral part.

If student did not pass the exam, he must repeat both written part and oral part next time.

Examination is checking knowledge of all topics read at the lecture and parts given to self-study by the course lecturer.

The exercises class credit is necessary to sign up for the exam.

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Alternative requirements to exam in crisis situation

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The exam is contained from a written part and an oral part.

  • Written part will be organized as a written test either in a full-time form or in a distance online form.
  • Oral part will be organized either in a full-time form or in a distance online form.

Written part is foregoing to oral part.

If written part is not fulfilled, whole exam is marked as non-satisfactory, and oral part is not treated.

Mark from the examination is determined considering results from both written and oral part.

If student did not pass the exam, he must repeat both written part and oral part next time.

Examination is checking knowledge of all topics specified by the course lecturer.

The exercises class credit is necessary to sign up for the exam.

Syllabus -
Last update: doc. RNDr. Petr Lachout, CSc. (27.04.2018)

1. Optimization problems and their formulations.

2. Selected parts of convex analyses (convex cones, convex function, epigraph, subdifferential).

3. Separation theorems (Farkas theorem).

4. Theory of nonlinear programming. (Karush-Kuhn-Tucker optimality condition, constraints qualifications).

5. Linear a convex programming like a particular case of nonlinear programming.

6. Symmetric problem of nonlinear programming.

Entry requirements -
Last update: doc. RNDr. Petr Lachout, CSc. (30.05.2018)

introduction to optimization theory, convex analysis, functional analysis

 
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