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Course, academic year 2023/2024
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Modern Algorithms in Numerical Optimisation - NMNV627
Title: Moderní algoritmy numerické optimalizace
Guaranteed by: Ústav teorie informace a automatizace AV ČR, v.v.i. (32-UTIAAV)
Faculty: Faculty of Mathematics and Physics
Actual: from 2020
Semester: winter
E-Credits: 3
Hours per week, examination: winter s.:2/0, Ex [HT]
Capacity: unlimited
Min. number of students: unlimited
4EU+: no
Virtual mobility / capacity: no
State of the course: not taught
Language: Czech
Teaching methods: full-time
Teaching methods: full-time
Guarantor: prof. Michal Kočvara, DrSc.
Class: DS, ekonometrie a operační výzkum
DS, vědecko - technické výpočty
Classification: Mathematics > Optimization
Incompatibility : NMOD038
Interchangeability : NMOD038
Is interchangeable with: NMOD038
Annotation -
Convex sets, convex functions. Elements of non-conditioned optimization. one-dimensional problems (line-search), methods of the type trust-region. Practical Newton's methods. Elements of conditioned optimization, optimality conditions. Quadratic programming, sequential quadratic programming. Methods of penalization and methods of an internal point for convex and non-convex conditioned optimization. Semidefinite programming.
Last update: G_M (19.06.2014)
Course completion requirements - Czech

Ke zkoušce není nutný zápočet. Zápočet bude udělen za docházku. Charakter zápočtu neumožňuje opravné termíny.

Last update: Kučera Václav, doc. RNDr., Ph.D. (14.06.2019)
Literature - Czech

Literatura: J. Nocedal, S. Wright: Numerical Optimization. Springer, 1999.

Last update: G_M (19.06.2014)
Requirements to the exam - Czech

Zkouška je ústní. Požadavky ke zkoušce odpovídají sylabu předmětu v rozsahu, který byl prezentován na přednášce.

Last update: Kučera Václav, doc. RNDr., Ph.D. (14.06.2019)
Syllabus -

Convex sets, convex functions. Elements of non-conditioned optimization. one-dimensional problems (line-search), methods of the type trust-region. Practical Newton's methods. Elements of conditioned optimization, optimality conditions. Quadratic programming, sequential quadratic programming. Methods of penalization and methods of an internal point for convex and non-convex conditioned optimization. Semidefinite programming.

Last update: G_M (19.06.2014)
 
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