M5170 Mathematical Programming

Faculty of Science
Autumn 2024
Extent and Intensity
2/2/0. 4 credit(s) (příf plus uk k 1 zk 2 plus 1 > 4). Type of Completion: zk (examination).
In-person direct teaching
Teacher(s)
doc. Mgr. Petr Zemánek, Ph.D. (lecturer)
Guaranteed by
doc. Mgr. Petr Zemánek, Ph.D.
Department of Mathematics and Statistics – Departments – Faculty of Science
Supplier department: Department of Mathematics and Statistics – Departments – Faculty of Science
Timetable
Tue 14:00–15:50 M2,01021
  • Timetable of Seminar Groups:
M5170/01: Fri 10:00–11:50 M3,01023, P. Zemánek
Course Enrolment Limitations
The course is also offered to the students of the fields other than those the course is directly associated with.
fields of study / plans the course is directly associated with
Course objectives
Students will get basic knowledge concerning mathematical programming, numerical methods of unconstrained optimization, and also convex analysis.
Learning outcomes
After passing the course, the student will be able:
(1) to define and interpret the basic notions used in the basic parts of convex analysis and to explain their mutual context;
(2) to formulate relevant mathematical theorems and statements and to explain methods of their proofs;
(3) to use effective techniques utilized in basic fields of convex analysis;
(4) to apply acquired pieces of knowledge for the solution of specific problems of convex programming and to some numerical methods of optimization including problems of applicative character.
Syllabus
  • I. Convex analysis: Convex sets (basic concepts, convex hull, separation and supporting hyperplanes); Convex Functions (basic concepts, criteria of convexity for differentiable functions); Subgradient and Subdifferential; Fenchel transformation; Systems of linear and convex inequalities.
  • II. Numerical methods of unconstrained minimization: One-dimensional minimization (brute-force search, dichotomous search, Fibonacci and golden ratio methods); Unconstrained optimization (Method of steepest descent, Newton method, Conjugate gradient method).
  • III. Mathematical programming: Lagrange principle (necessary and sufficient conditions for optimality, Kuhn-Tucker conditions, basic concepts of convex programming); Duality in mathematical programming (dual problem, Kuhn-Tucker vector, weak duality, strong duality, saddle point); Dependence on parameters (Envelope Theorem, shadow price)
Literature
    required literature
  • Petr Zemánek, Optimalizace aneb Když méně je více (učební text), viz https://optimalizace.page.link/ucebni_text
    recommended literature
  • DOŠLÝ, Ondřej. Základy konvexní analýzy a optimalizace v Rn. 1. vyd. Brno: Masarykova univerzita, 2005, viii, 185. ISBN 8021039051. info
  • HAMALA, Milan. Nelineárne programovanie. 2., dopl. vyd. Bratislava: Alfa, vydavateľstvo technickej a ekonomickej literatúry, 1976, 240 s. info
  • BERTSEKAS, Dimitri P. Convex Optimization Theory. Athena Scientific, 2009, 256 pp. ISBN 978-1-886529-31-1. info
  • Convex analysis. Edited by R. Tyrrell Rockafellar. Princeton: Princeton University Press, 1970, xviii, 451. ISBN 0691080690. info
  • BORWEIN, Jonathan M. and Adrian S. LEWIS. Convex analysis and nonlinear optimization : theory and examples. New York: Springer-Verlag, 2000, x, 273. ISBN 0387989404. info
  • SUN, Wenyu and Ya-Xiang YUAN. Optimization Theory and Methods - Nonlinear Programming. New York: Springer, 2006, 687 pp. Springer Optimization and Its Applications, Vol. 1. ISBN 978-0-387-24975-9. info
  • SUCHAREV, Aleksej Grigor‘jevič, Aleksandr Vasil'jevič TIMOCHOV and Vjačeslav Vasil'jevič FEDOROV. Kurs metodov optimizacii. Moskva: Nauka, 1986, 325 s. info
Teaching methods
Lectures and exercises.
Assessment methods
In order to be admitted to the exam, a semester project is required - the details are available in the study materials in IS. The standard lecture and seminar, the exam has both written and oral parts.

The conditions (especially regarding the form of the exam) will be specified according to the epidemiological situation and valid restrictions.
Language of instruction
Czech
Follow-Up Courses
Further comments (probably available only in Czech)
Study Materials
The course is taught annually.
The course is also listed under the following terms Autumn 2007 - for the purpose of the accreditation, Autumn 1999, Autumn 2010 - only for the accreditation, Autumn 2000, Autumn 2002, Autumn 2003, Autumn 2004, Autumn 2005, Autumn 2006, Autumn 2007, Autumn 2008, Autumn 2009, Autumn 2010, Autumn 2011, Autumn 2011 - acreditation, Autumn 2012, Autumn 2013, Autumn 2014, Autumn 2015, Autumn 2016, autumn 2017, Autumn 2018, Autumn 2019, Autumn 2020, autumn 2021, Autumn 2022, Autumn 2023.

M5170 Mathematical Programming

Faculty of Science
Autumn 2023
Extent and Intensity
2/2/0. 4 credit(s) (příf plus uk k 1 zk 2 plus 1 > 4). Type of Completion: zk (examination).
Teacher(s)
doc. Mgr. Petr Zemánek, Ph.D. (lecturer)
Guaranteed by
doc. Mgr. Petr Zemánek, Ph.D.
Department of Mathematics and Statistics – Departments – Faculty of Science
Supplier department: Department of Mathematics and Statistics – Departments – Faculty of Science
Timetable
Tue 12:00–13:50 M4,01024
  • Timetable of Seminar Groups:
M5170/01: Wed 10:00–11:50 M3,01023, P. Zemánek
Course Enrolment Limitations
The course is also offered to the students of the fields other than those the course is directly associated with.
fields of study / plans the course is directly associated with
Course objectives
Students will get basic knowledge concerning mathematical programming, numerical methods of unconstrained optimization, and also convex analysis.
Learning outcomes
After passing the course, the student will be able:
(1) to define and interpret the basic notions used in the basic parts of convex analysis and to explain their mutual context;
(2) to formulate relevant mathematical theorems and statements and to explain methods of their proofs;
(3) to use effective techniques utilized in basic fields of convex analysis;
(4) to apply acquired pieces of knowledge for the solution of specific problems of convex programming and to some numerical methods of optimization including problems of applicative character.
Syllabus
  • I. Convex analysis: Convex sets (basic concepts, convex hull, separation and supporting hyperplanes); Convex Functions (basic concepts, criteria of convexity for differentiable functions); Subgradient and Subdifferential; Fenchel transformation; Systems of linear and convex inequalities.
  • II. Numerical methods of unconstrained minimization: One-dimensional minimization (brute-force search, dichotomous search, Fibonacci and golden ratio methods); Unconstrained optimization (Method of steepest descent, Newton method, Conjugate gradient method).
  • III. Mathematical programming: Lagrange principle (necessary and sufficient conditions for optimality, Kuhn-Tucker conditions, basic concepts of convex programming); Duality in mathematical programming (dual problem, Kuhn-Tucker vector, weak duality, strong duality, saddle point); Dependence on parameters (Envelope Theorem, shadow price)
Literature
    required literature
  • Petr Zemánek, Optimalizace aneb Když méně je více (učební text), viz https://optimalizace.page.link/ucebni_text
    recommended literature
  • DOŠLÝ, Ondřej. Základy konvexní analýzy a optimalizace v Rn. 1. vyd. Brno: Masarykova univerzita, 2005, viii, 185. ISBN 8021039051. info
  • HAMALA, Milan. Nelineárne programovanie. 2., dopl. vyd. Bratislava: Alfa, vydavateľstvo technickej a ekonomickej literatúry, 1976, 240 s. info
  • BERTSEKAS, Dimitri P. Convex Optimization Theory. Athena Scientific, 2009, 256 pp. ISBN 978-1-886529-31-1. info
  • Convex analysis. Edited by R. Tyrrell Rockafellar. Princeton: Princeton University Press, 1970, xviii, 451. ISBN 0691080690. info
  • BORWEIN, Jonathan M. and Adrian S. LEWIS. Convex analysis and nonlinear optimization : theory and examples. New York: Springer-Verlag, 2000, x, 273. ISBN 0387989404. info
  • SUN, Wenyu and Ya-Xiang YUAN. Optimization Theory and Methods - Nonlinear Programming. New York: Springer, 2006, 687 pp. Springer Optimization and Its Applications, Vol. 1. ISBN 978-0-387-24975-9. info
  • SUCHAREV, Aleksej Grigor‘jevič, Aleksandr Vasil'jevič TIMOCHOV and Vjačeslav Vasil'jevič FEDOROV. Kurs metodov optimizacii. Moskva: Nauka, 1986, 325 s. info
Teaching methods
Lectures and exercises.
Assessment methods
In order to be admitted to the exam, a semester project is required - the details are available in the study materials in IS. The standard lecture and seminar, the exam has both written and oral parts.

The conditions (especially regarding the form of the exam) will be specified according to the epidemiological situation and valid restrictions.
Language of instruction
Czech
Follow-Up Courses
Further comments (probably available only in Czech)
Study Materials
The course is taught annually.
The course is also listed under the following terms Autumn 2007 - for the purpose of the accreditation, Autumn 1999, Autumn 2010 - only for the accreditation, Autumn 2000, Autumn 2002, Autumn 2003, Autumn 2004, Autumn 2005, Autumn 2006, Autumn 2007, Autumn 2008, Autumn 2009, Autumn 2010, Autumn 2011, Autumn 2011 - acreditation, Autumn 2012, Autumn 2013, Autumn 2014, Autumn 2015, Autumn 2016, autumn 2017, Autumn 2018, Autumn 2019, Autumn 2020, autumn 2021, Autumn 2022, Autumn 2024.

M5170 Mathematical Programming

Faculty of Science
Autumn 2022
Extent and Intensity
2/2/0. 4 credit(s) (příf plus uk k 1 zk 2 plus 1 > 4). Type of Completion: zk (examination).
Teacher(s)
prof. RNDr. Roman Šimon Hilscher, DSc. (lecturer)
doc. Mgr. Petr Zemánek, Ph.D. (lecturer)
Guaranteed by
prof. RNDr. Roman Šimon Hilscher, DSc.
Department of Mathematics and Statistics – Departments – Faculty of Science
Supplier department: Department of Mathematics and Statistics – Departments – Faculty of Science
Timetable
Mon 10:00–11:50 M2,01021
  • Timetable of Seminar Groups:
M5170/01: Wed 10:00–11:50 M4,01024, P. Zemánek
Course Enrolment Limitations
The course is also offered to the students of the fields other than those the course is directly associated with.
fields of study / plans the course is directly associated with
Course objectives
Students will get basic knowledge concerning mathematical programming, numerical methods of unconstrained optimization, and also convex analysis.
Learning outcomes
After passing the course, the student will be able:
(1) to define and interpret the basic notions used in the basic parts of convex analysis and to explain their mutual context;
(2) to formulate relevant mathematical theorems and statements and to explain methods of their proofs;
(3) to use effective techniques utilized in basic fields of convex analysis;
(4) to apply acquired pieces of knowledge for the solution of specific problems of convex programming and to some numerical methods of optimization including problems of applicative character.
Syllabus
  • I. Convex analysis: Convex sets (basic concepts, convex hull, separation and supporting hyperplanes); Convex Functions (basic concepts, criteria of convexity for differentiable functions); Subgradient and Subdifferential; Fenchel transformation; Systems of linear and convex inequalities.
  • II. Numerical methods of unconstrained minimization: One-dimensional minimization (brute-force search, dichotomous search, Fibonacci and golden ratio methods); Unconstrained optimization (Method of steepest descent, Newton method, Conjugate gradient method).
  • III. Mathematical programming: Lagrange principle (necessary and sufficient conditions for optimality, Kuhn-Tucker conditions, basic concepts of convex programming); Duality in mathematical programming (dual problem, Kuhn-Tucker vector, weak duality, strong duality, saddle point); Dependence on parameters (Envelope Theorem, shadow price)
Literature
    required literature
  • Petr Zemánek, Optimalizace aneb Když méně je více (učební text), viz https://optimalizace.page.link/ucebni_text
    recommended literature
  • DOŠLÝ, Ondřej. Základy konvexní analýzy a optimalizace v Rn. 1. vyd. Brno: Masarykova univerzita, 2005, viii, 185. ISBN 8021039051. info
  • HAMALA, Milan. Nelineárne programovanie. 2., dopl. vyd. Bratislava: Alfa, vydavateľstvo technickej a ekonomickej literatúry, 1976, 240 s. info
  • BERTSEKAS, Dimitri P. Convex Optimization Theory. Athena Scientific, 2009, 256 pp. ISBN 978-1-886529-31-1. info
  • Convex analysis. Edited by R. Tyrrell Rockafellar. Princeton: Princeton University Press, 1970, xviii, 451. ISBN 0691080690. info
  • BORWEIN, Jonathan M. and Adrian S. LEWIS. Convex analysis and nonlinear optimization : theory and examples. New York: Springer-Verlag, 2000, x, 273. ISBN 0387989404. info
  • SUN, Wenyu and Ya-Xiang YUAN. Optimization Theory and Methods - Nonlinear Programming. New York: Springer, 2006, 687 pp. Springer Optimization and Its Applications, Vol. 1. ISBN 978-0-387-24975-9. info
  • SUCHAREV, Aleksej Grigor‘jevič, Aleksandr Vasil'jevič TIMOCHOV and Vjačeslav Vasil'jevič FEDOROV. Kurs metodov optimizacii. Moskva: Nauka, 1986, 325 s. info
Teaching methods
Lectures and exercises.
Assessment methods
In order to be admitted to the exam, a semester project is required - the details are available in the study materials in IS. The standard lecture and seminar, the exam has both written and oral parts.

The conditions (especially regarding the form of the exam) will be specified according to the epidemiological situation and valid restrictions.
Language of instruction
Czech
Follow-Up Courses
Further comments (probably available only in Czech)
Study Materials
The course is taught annually.
The course is also listed under the following terms Autumn 2007 - for the purpose of the accreditation, Autumn 1999, Autumn 2010 - only for the accreditation, Autumn 2000, Autumn 2002, Autumn 2003, Autumn 2004, Autumn 2005, Autumn 2006, Autumn 2007, Autumn 2008, Autumn 2009, Autumn 2010, Autumn 2011, Autumn 2011 - acreditation, Autumn 2012, Autumn 2013, Autumn 2014, Autumn 2015, Autumn 2016, autumn 2017, Autumn 2018, Autumn 2019, Autumn 2020, autumn 2021, Autumn 2023, Autumn 2024.

M5170 Mathematical Programming

Faculty of Science
autumn 2021
Extent and Intensity
2/2/0. 4 credit(s) (příf plus uk k 1 zk 2 plus 1 > 4). Type of Completion: zk (examination).
Teacher(s)
doc. Mgr. Petr Zemánek, Ph.D. (lecturer)
Guaranteed by
prof. RNDr. Roman Šimon Hilscher, DSc.
Department of Mathematics and Statistics – Departments – Faculty of Science
Supplier department: Department of Mathematics and Statistics – Departments – Faculty of Science
Timetable
Mon 10:00–11:50 MP2,01014a
  • Timetable of Seminar Groups:
M5170/01: Tue 12:00–13:50 M6,01011, P. Zemánek
Course Enrolment Limitations
The course is also offered to the students of the fields other than those the course is directly associated with.
fields of study / plans the course is directly associated with
Course objectives
Students will get basic knowledge concerning mathematical programming, numerical methods of unconstrained optimization, and also convex analysis.
Learning outcomes
After passing the course, the student will be able:
(1) to define and interpret the basic notions used in the basic parts of convex analysis and to explain their mutual context;
(2) to formulate relevant mathematical theorems and statements and to explain methods of their proofs;
(3) to use effective techniques utilized in basic fields of convex analysis;
(4) to apply acquired pieces of knowledge for the solution of specific problems of convex programming and to some numerical methods of optimization including problems of applicative character.
Syllabus
  • I. Convex analysis: Convex sets (basic concepts, convex hull, separation and supporting hyperplanes); Convex Functions (basic concepts, criteria of convexity for differentiable functions); Subgradient and Subdifferential; Fenchel transformation; Systems of linear and convex inequalities.
  • II. Numerical methods of unconstrained minimization: One-dimensional minimization (brute-force search, dichotomous search, Fibonacci and golden ratio methods); Unconstrained optimization (Method of steepest descent, Newton method, Conjugate gradient method).
  • III. Mathematical programming: Lagrange principle (necessary and sufficient conditions for optimality, Kuhn-Tucker conditions, basic concepts of convex programming); Duality in mathematical programming (dual problem, Kuhn-Tucker vector, weak duality, strong duality, saddle point); Dependence on parameters (Envelope Theorem, shadow price)
Literature
    recommended literature
  • DOŠLÝ, Ondřej. Základy konvexní analýzy a optimalizace v Rn. 1. vyd. Brno: Masarykova univerzita, 2005, viii, 185. ISBN 8021039051. info
  • HAMALA, Milan. Nelineárne programovanie. 2., dopl. vyd. Bratislava: Alfa, vydavateľstvo technickej a ekonomickej literatúry, 1976, 240 s. info
  • BERTSEKAS, Dimitri P. Convex Optimization Theory. Athena Scientific, 2009, 256 pp. ISBN 978-1-886529-31-1. info
  • Convex analysis. Edited by R. Tyrrell Rockafellar. Princeton: Princeton University Press, 1970, xviii, 451. ISBN 0691080690. info
  • BORWEIN, Jonathan M. and Adrian S. LEWIS. Convex analysis and nonlinear optimization : theory and examples. New York: Springer-Verlag, 2000, x, 273. ISBN 0387989404. info
  • SUN, Wenyu and Ya-Xiang YUAN. Optimization Theory and Methods - Nonlinear Programming. New York: Springer, 2006, 687 pp. Springer Optimization and Its Applications, Vol. 1. ISBN 978-0-387-24975-9. info
  • SUCHAREV, Aleksej Grigor‘jevič, Aleksandr Vasil'jevič TIMOCHOV and Vjačeslav Vasil'jevič FEDOROV. Kurs metodov optimizacii. Moskva: Nauka, 1986, 325 s. info
Teaching methods
Lectures and exercises.
Assessment methods
In order to be admitted to the exam, a semester project is required - the details are available in the study materials in IS. The standard lecture and seminar, the exam has both written and oral parts.

The conditions (especially regarding the form of the exam) will be specified according to the epidemiological situation and valid restrictions.
Language of instruction
Czech
Follow-Up Courses
Further comments (probably available only in Czech)
Study Materials
The course is taught annually.
The course is also listed under the following terms Autumn 2007 - for the purpose of the accreditation, Autumn 1999, Autumn 2010 - only for the accreditation, Autumn 2000, Autumn 2002, Autumn 2003, Autumn 2004, Autumn 2005, Autumn 2006, Autumn 2007, Autumn 2008, Autumn 2009, Autumn 2010, Autumn 2011, Autumn 2011 - acreditation, Autumn 2012, Autumn 2013, Autumn 2014, Autumn 2015, Autumn 2016, autumn 2017, Autumn 2018, Autumn 2019, Autumn 2020, Autumn 2022, Autumn 2023, Autumn 2024.

M5170 Mathematical Programming

Faculty of Science
Autumn 2020
Extent and Intensity
2/2/0. 4 credit(s) (příf plus uk k 1 zk 2 plus 1 > 4). Type of Completion: zk (examination).
Teacher(s)
doc. Mgr. Petr Zemánek, Ph.D. (lecturer)
Guaranteed by
prof. RNDr. Roman Šimon Hilscher, DSc.
Department of Mathematics and Statistics – Departments – Faculty of Science
Supplier department: Department of Mathematics and Statistics – Departments – Faculty of Science
Timetable
Tue 8:00–9:50 M1,01017
  • Timetable of Seminar Groups:
M5170/01: Wed 14:00–15:50 M1,01017, P. Zemánek
Course Enrolment Limitations
The course is also offered to the students of the fields other than those the course is directly associated with.
fields of study / plans the course is directly associated with
Course objectives
Students will get basic knowledge concerning mathematical programming, numerical methods of unconstrained optimization, and also convex analysis.
Learning outcomes
After passing the course, the student will be able:
(1) to define and interpret the basic notions used in the basic parts of convex analysis and to explain their mutual context;
(2) to formulate relevant mathematical theorems and statements and to explain methods of their proofs;
(3) to use effective techniques utilized in basic fields of convex analysis;
(4) to apply acquired pieces of knowledge for the solution of specific problems of convex programming and to some numerical methods of optimization including problems of applicative character.
Syllabus
  • I. Convex analysis: Convex sets (basic concepts, convex hull, separation and supporting hyperplanes); Convex Functions (basic concepts, criteria of convexity for differentiable functions); Subgradient and Subdifferential; Fenchel transformation; Systems of linear and convex inequalities.
  • II. Numerical methods of unconstrained minimization: One-dimensional minimization (brute-force search, dichotomous search, Fibonacci and golden ratio methods); Unconstrained optimization (Method of steepest descent, Newton method, Conjugate gradient method).
  • III. Mathematical programming: Lagrange principle (necessary and sufficient conditions for optimality, Kuhn-Tucker conditions, basic concepts of convex programming); Duality in mathematical programming (dual problem, Kuhn-Tucker vector, weak duality, strong duality, saddle point); Dependence on parameters (Envelope Theorem, shadow price)
Literature
    recommended literature
  • DOŠLÝ, Ondřej. Základy konvexní analýzy a optimalizace v Rn. 1. vyd. Brno: Masarykova univerzita, 2005, viii, 185. ISBN 8021039051. info
  • HAMALA, Milan. Nelineárne programovanie. 2., dopl. vyd. Bratislava: Alfa, vydavateľstvo technickej a ekonomickej literatúry, 1976, 240 s. info
  • BERTSEKAS, Dimitri P. Convex Optimization Theory. Athena Scientific, 2009, 256 pp. ISBN 978-1-886529-31-1. info
  • Convex analysis. Edited by R. Tyrrell Rockafellar. Princeton: Princeton University Press, 1970, xviii, 451. ISBN 0691080690. info
  • BORWEIN, Jonathan M. and Adrian S. LEWIS. Convex analysis and nonlinear optimization : theory and examples. New York: Springer-Verlag, 2000, x, 273. ISBN 0387989404. info
  • SUN, Wenyu and Ya-Xiang YUAN. Optimization Theory and Methods - Nonlinear Programming. New York: Springer, 2006, 687 pp. Springer Optimization and Its Applications, Vol. 1. ISBN 978-0-387-24975-9. info
  • SUCHAREV, Aleksej Grigor‘jevič, Aleksandr Vasil'jevič TIMOCHOV and Vjačeslav Vasil'jevič FEDOROV. Kurs metodov optimizacii. Moskva: Nauka, 1986, 325 s. info
Teaching methods
Lectures and exercises.
Assessment methods
In order to be admitted to the exam, a semester project is required - the details are available in the study materials in IS. The standard lecture and seminar, the exam has both written and oral parts.

The conditions (especially regarding the form of the exam) will be specified according to the epidemiological situation and valid restrictions.
Language of instruction
Czech
Follow-Up Courses
Further comments (probably available only in Czech)
Study Materials
The course is taught annually.
The course is also listed under the following terms Autumn 2007 - for the purpose of the accreditation, Autumn 1999, Autumn 2010 - only for the accreditation, Autumn 2000, Autumn 2002, Autumn 2003, Autumn 2004, Autumn 2005, Autumn 2006, Autumn 2007, Autumn 2008, Autumn 2009, Autumn 2010, Autumn 2011, Autumn 2011 - acreditation, Autumn 2012, Autumn 2013, Autumn 2014, Autumn 2015, Autumn 2016, autumn 2017, Autumn 2018, Autumn 2019, autumn 2021, Autumn 2022, Autumn 2023, Autumn 2024.

M5170 Mathematical Programming

Faculty of Science
Autumn 2019
Extent and Intensity
2/2/0. 4 credit(s) (příf plus uk k 1 zk 2 plus 1 > 4). Type of Completion: zk (examination).
Teacher(s)
doc. Mgr. Petr Zemánek, Ph.D. (lecturer)
Guaranteed by
prof. RNDr. Roman Šimon Hilscher, DSc.
Department of Mathematics and Statistics – Departments – Faculty of Science
Supplier department: Department of Mathematics and Statistics – Departments – Faculty of Science
Timetable
Fri 10:00–11:50 M1,01017
  • Timetable of Seminar Groups:
M5170/01: Fri 12:00–13:50 M1,01017, P. Zemánek
Course Enrolment Limitations
The course is also offered to the students of the fields other than those the course is directly associated with.
fields of study / plans the course is directly associated with
Course objectives
Students will get basic knowledge concerning mathematical programming, numerical methods of unconstrained optimization, and also convex analysis.
Learning outcomes
After passing the course, the student will be able:
(1) to define and interpret the basic notions used in the basic parts of convex analysis and to explain their mutual context;
(2) to formulate relevant mathematical theorems and statements and to explain methods of their proofs;
(3) to use effective techniques utilized in basic fields of convex analysis;
(4) to apply acquired pieces of knowledge for the solution of specific problems of convex programming and to some numerical methods of optimization including problems of applicative character.
Syllabus
  • I. Convex analysis: Convex sets (basic concepts, convex hull, separation and supporting hyperplanes); Convex Functions (basic concepts, criteria of convexity for differentiable functions); Subgradient and Subdifferential; Fenchel transformation; Systems of linear and convex inequalities.
  • II. Numerical methods of unconstrained minimization: One-dimensional minimization (brute-force search, dichotomous search, Fibonacci and golden ratio methods); Unconstrained optimization (Method of steepest descent, Newton method, Conjugate gradient method).
  • III. Mathematical programming: Lagrange principle (necessary and sufficient conditions for optimality, Kuhn-Tucker conditions, basic concepts of convex programming); Duality in mathematical programming (dual problem, Kuhn-Tucker vector, weak duality, strong duality, saddle point); Dependence on parameters (Envelope Theorem, shadow price)
Literature
    recommended literature
  • DOŠLÝ, Ondřej. Základy konvexní analýzy a optimalizace v Rn. 1. vyd. Brno: Masarykova univerzita, 2005, viii, 185. ISBN 8021039051. info
  • HAMALA, Milan. Nelineárne programovanie. 2., dopl. vyd. Bratislava: Alfa, vydavateľstvo technickej a ekonomickej literatúry, 1976, 240 s. info
  • BERTSEKAS, Dimitri P. Convex Optimization Theory. Athena Scientific, 2009, 256 pp. ISBN 978-1-886529-31-1. info
  • Convex analysis. Edited by R. Tyrrell Rockafellar. Princeton: Princeton University Press, 1970, xviii, 451. ISBN 0691080690. info
  • BORWEIN, Jonathan M. and Adrian S. LEWIS. Convex analysis and nonlinear optimization : theory and examples. New York: Springer-Verlag, 2000, x, 273. ISBN 0387989404. info
  • SUN, Wenyu and Ya-Xiang YUAN. Optimization Theory and Methods - Nonlinear Programming. New York: Springer, 2006, 687 pp. Springer Optimization and Its Applications, Vol. 1. ISBN 978-0-387-24975-9. info
  • SUCHAREV, Aleksej Grigor‘jevič, Aleksandr Vasil'jevič TIMOCHOV and Vjačeslav Vasil'jevič FEDOROV. Kurs metodov optimizacii. Moskva: Nauka, 1986, 325 s. info
Teaching methods
Lectures and exercises.
Assessment methods
In order to be admitted to the exam, a semester project is required - the details are available in the study materials in IS. The standard lecture and seminar, the exam has both written and oral parts.
Language of instruction
Czech
Follow-Up Courses
Further comments (probably available only in Czech)
Study Materials
The course is taught annually.
The course is also listed under the following terms Autumn 2007 - for the purpose of the accreditation, Autumn 1999, Autumn 2010 - only for the accreditation, Autumn 2000, Autumn 2002, Autumn 2003, Autumn 2004, Autumn 2005, Autumn 2006, Autumn 2007, Autumn 2008, Autumn 2009, Autumn 2010, Autumn 2011, Autumn 2011 - acreditation, Autumn 2012, Autumn 2013, Autumn 2014, Autumn 2015, Autumn 2016, autumn 2017, Autumn 2018, Autumn 2020, autumn 2021, Autumn 2022, Autumn 2023, Autumn 2024.

M5170 Mathematical Programming

Faculty of Science
Autumn 2018
Extent and Intensity
2/1/0. 3 credit(s) (příf plus uk k 1 zk 2 plus 1 > 4). Type of Completion: zk (examination).
Teacher(s)
doc. Mgr. Petr Zemánek, Ph.D. (lecturer)
Guaranteed by
prof. RNDr. Roman Šimon Hilscher, DSc.
Department of Mathematics and Statistics – Departments – Faculty of Science
Supplier department: Department of Mathematics and Statistics – Departments – Faculty of Science
Timetable
Mon 17. 9. to Fri 14. 12. Mon 14:00–15:50 M2,01021
  • Timetable of Seminar Groups:
M5170/01: Mon 17. 9. to Fri 14. 12. Mon 16:00–16:50 M2,01021, P. Zemánek
M5170/02: Mon 17. 9. to Fri 14. 12. Tue 15:00–15:50 M3,01023, P. Zemánek
Course Enrolment Limitations
The course is also offered to the students of the fields other than those the course is directly associated with.
fields of study / plans the course is directly associated with
Course objectives
After passing the course, the student will be able:
(1) to define and interpret the basic notions used in the basic parts of convex analysis and to explain their mutual context;
(2) to formulate relevant mathematical theorems and statements and to explain methods of their proofs;
(3) to use effective techniques utilized in basic fields of convex analysis;
(4) to apply acquired pieces of knowledge for the solution of specific problems of convex programming and to some numerical methods of optimization including problems of applicative character.
Syllabus
  • I. Convex analysis: Convex sets (basic concepts, convex hull, separation and supporting hyperplanes); Convex Functions (basic concepts, criteria of convexity for differentiable functions); Subgradient and Subdifferential; Fenchel transformation; Systems of linear and convex inequalities.
  • II. Numerical methods of unconstrained minimization: One-dimensional minimization (brute-force search, dichotomous search, Fibonacci and golden ratio methods); Unconstrained optimization (Method of steepest descent, Newton method, Conjugate gradient method).
  • III. Mathematical programming: Lagrange principle (necessary and sufficient conditions for optimality, Kuhn-Tucker conditions, basic concepts of convex programming); Duality in mathematical programming (dual problem, Kuhn-Tucker vector, weak duality, strong duality, saddle point); Dependence on parameters (Envelope Theorem, shadow price)
Literature
    recommended literature
  • DOŠLÝ, Ondřej. Základy konvexní analýzy a optimalizace v Rn. 1. vyd. Brno: Masarykova univerzita, 2005, viii, 185. ISBN 8021039051. info
  • HAMALA, Milan. Nelineárne programovanie. 2., dopl. vyd. Bratislava: Alfa, vydavateľstvo technickej a ekonomickej literatúry, 1976, 240 s. info
  • BERTSEKAS, Dimitri P. Convex Optimization Theory. Athena Scientific, 2009, 256 pp. ISBN 978-1-886529-31-1. info
  • Convex analysis. Edited by R. Tyrrell Rockafellar. Princeton: Princeton University Press, 1970, xviii, 451. ISBN 0691080690. info
  • BORWEIN, Jonathan M. and Adrian S. LEWIS. Convex analysis and nonlinear optimization : theory and examples. New York: Springer-Verlag, 2000, x, 273. ISBN 0387989404. info
  • SUN, Wenyu and Ya-Xiang YUAN. Optimization Theory and Methods - Nonlinear Programming. New York: Springer, 2006, 687 pp. Springer Optimization and Its Applications, Vol. 1. ISBN 978-0-387-24975-9. info
  • SUCHAREV, Aleksej Grigor‘jevič, Aleksandr Vasil'jevič TIMOCHOV and Vjačeslav Vasil'jevič FEDOROV. Kurs metodov optimizacii. Moskva: Nauka, 1986, 325 s. info
Teaching methods
Theoretical lecture and seminar
Assessment methods
In order to be admitted to the exam, a semester project is required - the details are available in the study materials in IS. The standard lecture and seminar, the exam has both written and oral parts.
Language of instruction
Czech
Follow-Up Courses
Further comments (probably available only in Czech)
Study Materials
The course is taught annually.
The course is also listed under the following terms Autumn 2007 - for the purpose of the accreditation, Autumn 1999, Autumn 2010 - only for the accreditation, Autumn 2000, Autumn 2002, Autumn 2003, Autumn 2004, Autumn 2005, Autumn 2006, Autumn 2007, Autumn 2008, Autumn 2009, Autumn 2010, Autumn 2011, Autumn 2011 - acreditation, Autumn 2012, Autumn 2013, Autumn 2014, Autumn 2015, Autumn 2016, autumn 2017, Autumn 2019, Autumn 2020, autumn 2021, Autumn 2022, Autumn 2023, Autumn 2024.

M5170 Mathematical Programming

Faculty of Science
autumn 2017
Extent and Intensity
2/1/0. 3 credit(s) (příf plus uk k 1 zk 2 plus 1 > 4). Type of Completion: zk (examination).
Teacher(s)
doc. Mgr. Petr Zemánek, Ph.D. (lecturer)
Guaranteed by
prof. RNDr. Roman Šimon Hilscher, DSc.
Department of Mathematics and Statistics – Departments – Faculty of Science
Supplier department: Department of Mathematics and Statistics – Departments – Faculty of Science
Timetable
Mon 18. 9. to Fri 15. 12. Tue 18:00–19:50 M1,01017
  • Timetable of Seminar Groups:
M5170/01: Mon 18. 9. to Fri 15. 12. Wed 13:00–13:50 M5,01013, P. Zemánek
M5170/02: Mon 18. 9. to Fri 15. 12. Wed 17:00–17:50 M6,01011, P. Zemánek
Course Enrolment Limitations
The course is also offered to the students of the fields other than those the course is directly associated with.
fields of study / plans the course is directly associated with
Course objectives
After passing the course, the student will be able:
(1) to define and interpret the basic notions used in the basic parts of convex analysis and to explain their mutual context;
(2) to formulate relevant mathematical theorems and statements and to explain methods of their proofs;
(3) to use effective techniques utilized in basic fields of convex analysis;
(4) to apply acquired pieces of knowledge for the solution of specific problems of convex programming and to some numerical methods of optimization including problems of applicative character.
Syllabus
  • I. Convex analysis: Convex sets (basic concepts, convex hull, separation and supporting hyperplanes); Convex Functions (basic concepts, criteria of convexity for differentiable functions); Subgradient and Subdifferential; Fenchel transformation; Systems of linear and convex inequalities.
  • II. Numerical methods of unconstrained minimization: One-dimensional minimization (brute-force search, dichotomous search, Fibonacci and golden ratio methods); Unconstrained optimization (Method of steepest descent, Newton method, Conjugate gradient method).
  • III. Mathematical programming: Lagrange principle (necessary and sufficient conditions for optimality, Kuhn-Tucker conditions, basic concepts of convex programming); Duality in mathematical programming (dual problem, Kuhn-Tucker vector, weak duality, strong duality, saddle point); Dependence on parameters (Envelope Theorem, shadow price)
Literature
    recommended literature
  • DOŠLÝ, Ondřej. Základy konvexní analýzy a optimalizace v Rn. 1. vyd. Brno: Masarykova univerzita, 2005, viii, 185. ISBN 8021039051. info
  • HAMALA, Milan. Nelineárne programovanie. 2., dopl. vyd. Bratislava: Alfa, vydavateľstvo technickej a ekonomickej literatúry, 1976, 240 s. info
  • BERTSEKAS, Dimitri P. Convex Optimization Theory. Athena Scientific, 2009, 256 pp. ISBN 978-1-886529-31-1. info
  • Convex analysis. Edited by R. Tyrrell Rockafellar. Princeton: Princeton University Press, 1970, xviii, 451. ISBN 0691080690. info
  • BORWEIN, Jonathan M. and Adrian S. LEWIS. Convex analysis and nonlinear optimization : theory and examples. New York: Springer-Verlag, 2000, x, 273. ISBN 0387989404. info
  • SUN, Wenyu and Ya-Xiang YUAN. Optimization Theory and Methods - Nonlinear Programming. New York: Springer, 2006, 687 pp. Springer Optimization and Its Applications, Vol. 1. ISBN 978-0-387-24975-9. info
  • SUCHAREV, Aleksej Grigor‘jevič, Aleksandr Vasil'jevič TIMOCHOV and Vjačeslav Vasil'jevič FEDOROV. Kurs metodov optimizacii. Moskva: Nauka, 1986, 325 s. info
Teaching methods
Theoretical lecture and seminar
Assessment methods
In order to be admitted to the exam, a semester project is required - the details are available in the study materials in IS. The standard lecture and seminar, the exam has both written and oral parts.
Language of instruction
Czech
Follow-Up Courses
Further comments (probably available only in Czech)
Study Materials
The course is taught annually.
The course is also listed under the following terms Autumn 2007 - for the purpose of the accreditation, Autumn 1999, Autumn 2010 - only for the accreditation, Autumn 2000, Autumn 2002, Autumn 2003, Autumn 2004, Autumn 2005, Autumn 2006, Autumn 2007, Autumn 2008, Autumn 2009, Autumn 2010, Autumn 2011, Autumn 2011 - acreditation, Autumn 2012, Autumn 2013, Autumn 2014, Autumn 2015, Autumn 2016, Autumn 2018, Autumn 2019, Autumn 2020, autumn 2021, Autumn 2022, Autumn 2023, Autumn 2024.

M5170 Mathematical Programming

Faculty of Science
Autumn 2016
Extent and Intensity
2/1/0. 3 credit(s) (příf plus uk k 1 zk 2 plus 1 > 4). Type of Completion: zk (examination).
Teacher(s)
doc. Mgr. Petr Zemánek, Ph.D. (lecturer)
Guaranteed by
prof. RNDr. Roman Šimon Hilscher, DSc.
Department of Mathematics and Statistics – Departments – Faculty of Science
Supplier department: Department of Mathematics and Statistics – Departments – Faculty of Science
Timetable
Mon 19. 9. to Sun 18. 12. Wed 8:00–9:50 M1,01017
  • Timetable of Seminar Groups:
M5170/01: Mon 19. 9. to Sun 18. 12. Fri 11:00–11:50 M2,01021, P. Zemánek
M5170/02: Mon 19. 9. to Sun 18. 12. Fri 12:00–12:50 M1,01017, P. Zemánek
Course Enrolment Limitations
The course is also offered to the students of the fields other than those the course is directly associated with.
fields of study / plans the course is directly associated with
Course objectives
After passing the course, the student will be able:
(1) to define and interpret the basic notions used in the basic parts of convex analysis and to explain their mutual context;
(2) to formulate relevant mathematical theorems and statements and to explain methods of their proofs;
(3) to use effective techniques utilized in basic fields of convex analysis;
(4) to apply acquired pieces of knowledge for the solution of specific problems of convex programming and to some numerical methods of optimization including problems of applicative character.
Syllabus
  • I. Convex analysis: Convex sets (basic concepts, convex hull, separation and supporting hyperplanes); Convex Functions (basic concepts, criteria of convexity for differentiable functions); Subgradient and Subdifferential; Fenchel transformation; Systems of linear and convex inequalities.
  • II. Numerical methods of unconstrained minimization: One-dimensional minimization (brute-force search, dichotomous search, Fibonacci and golden ratio methods); Unconstrained optimization (Method of steepest descent, Newton method, Conjugate gradient method).
  • III. Mathematical programming: Lagrange principle (necessary and sufficient conditions for optimality, Kuhn-Tucker conditions, basic concepts of convex programming); Duality in mathematical programming (dual problem, Kuhn-Tucker vector, weak duality, strong duality, saddle point); Dependence on parameters (Envelope Theorem, shadow price)
Literature
    recommended literature
  • DOŠLÝ, Ondřej. Základy konvexní analýzy a optimalizace v Rn. 1. vyd. Brno: Masarykova univerzita, 2005, viii, 185. ISBN 8021039051. info
  • HAMALA, Milan. Nelineárne programovanie. 2., dopl. vyd. Bratislava: Alfa, vydavateľstvo technickej a ekonomickej literatúry, 1976, 240 s. info
  • BERTSEKAS, Dimitri P. Convex Optimization Theory. Athena Scientific, 2009, 256 pp. ISBN 978-1-886529-31-1. info
  • Convex analysis. Edited by R. Tyrrell Rockafellar. Princeton: Princeton University Press, 1970, xviii, 451. ISBN 0691080690. info
  • BORWEIN, Jonathan M. and Adrian S. LEWIS. Convex analysis and nonlinear optimization : theory and examples. New York: Springer-Verlag, 2000, x, 273. ISBN 0387989404. info
  • SUN, Wenyu and Ya-Xiang YUAN. Optimization Theory and Methods - Nonlinear Programming. New York: Springer, 2006, 687 pp. Springer Optimization and Its Applications, Vol. 1. ISBN 978-0-387-24975-9. info
  • SUCHAREV, Aleksej Grigor‘jevič, Aleksandr Vasil'jevič TIMOCHOV and Vjačeslav Vasil'jevič FEDOROV. Kurs metodov optimizacii. Moskva: Nauka, 1986, 325 s. info
Teaching methods
Theoretical lecture and seminar
Assessment methods
In order to be admitted to the exam, a semester project is required - the details are available in the study materials in IS. The standard lecture and seminar, the exam has both written and oral parts.
Language of instruction
Czech
Follow-Up Courses
Further comments (probably available only in Czech)
Study Materials
The course is taught annually.
The course is also listed under the following terms Autumn 2007 - for the purpose of the accreditation, Autumn 1999, Autumn 2010 - only for the accreditation, Autumn 2000, Autumn 2002, Autumn 2003, Autumn 2004, Autumn 2005, Autumn 2006, Autumn 2007, Autumn 2008, Autumn 2009, Autumn 2010, Autumn 2011, Autumn 2011 - acreditation, Autumn 2012, Autumn 2013, Autumn 2014, Autumn 2015, autumn 2017, Autumn 2018, Autumn 2019, Autumn 2020, autumn 2021, Autumn 2022, Autumn 2023, Autumn 2024.

M5170 Mathematical Programming

Faculty of Science
Autumn 2015
Extent and Intensity
2/1/0. 3 credit(s) (příf plus uk k 1 zk 2 plus 1 > 4). Type of Completion: zk (examination).
Teacher(s)
doc. Mgr. Petr Zemánek, Ph.D. (lecturer)
Guaranteed by
prof. RNDr. Ondřej Došlý, DrSc.
Department of Mathematics and Statistics – Departments – Faculty of Science
Supplier department: Department of Mathematics and Statistics – Departments – Faculty of Science
Timetable
Mon 8:00–9:50 M1,01017
  • Timetable of Seminar Groups:
M5170/01: Tue 15:00–15:50 M5,01013, P. Zemánek
M5170/02: Tue 14:00–14:50 M5,01013, P. Zemánek
Course Enrolment Limitations
The course is also offered to the students of the fields other than those the course is directly associated with.
fields of study / plans the course is directly associated with
Course objectives
After passing the course, the student will be able:
to define and interpret the basic notions used in the basic parts of convex analysis and to explain their mutual context;
to formulate relevant mathematical theorems and statements and to explain methods of their proofs;
to use effective techniques utilized in basic fields of convex analysis;
to apply acquired pieces of knowledge for the solution of specific problems of convex programming and to some numerical methods of optimization including problems of applicative character.
Syllabus
  • I. Convex analysis: Convex sets (basic concepts, convex hull, separation and supporting hyperplanes); Convex Functions (basic concepts, criteria of convexity for differentiable functions); Subgradient and Subdifferential; Fenchel transformation; Systems of linear and convex inequalities.
  • II. Mathematical programming, necessary and sufficient conditions for optimality, duality: Lagrange principle (Kuhn-Tucker conditions, basic concepts of convex progamming); Duality in mathematical programming (dual problem, Kuhn-Tucker vector, saddle point); Duality in special optimization problems (linear and quadratic)
  • III. Numerical methods of minimization: One-dimensional minimization (Fibonaci and golden ratio methods); Unconstrained optimization (steepest slope method, method of conjugate gradients, Newton method).
Literature
  • DOŠLÝ, Ondřej. Základy konvexní analýzy a optimalizace v Rn. 1. vyd. Brno: Masarykova univerzita, 2005, viii, 185. ISBN 8021039051. info
  • HAMALA, Milan. Nelineárne programovanie. 2., dopl. vyd. Bratislava: Alfa, vydavateľstvo technickej a ekonomickej literatúry, 1976, 240 s. info
  • BERTSEKAS, Dimitri P. Convex Optimization Theory. Athena Scientific, 2009, 256 pp. ISBN 978-1-886529-31-1. info
  • Convex analysis. Edited by R. Tyrrell Rockafellar. Princeton: Princeton University Press, 1970, xviii, 451. ISBN 0691080690. info
  • BORWEIN, Jonathan M. and Adrian S. LEWIS. Convex analysis and nonlinear optimization : theory and examples. New York: Springer-Verlag, 2000, x, 273. ISBN 0387989404. info
  • SUCHAREV, Aleksej Grigor‘jevič, Aleksandr Vasil'jevič TIMOCHOV and Vjačeslav Vasil'jevič FEDOROV. Kurs metodov optimizacii. Moskva: Nauka, 1986, 325 s. info
Teaching methods
Theoretical lecture and seminar
Assessment methods
The standard lecture and seminar, the exam has both written and oral parts.
Language of instruction
Czech
Follow-Up Courses
Further comments (probably available only in Czech)
Study Materials
The course is taught annually.
The course is also listed under the following terms Autumn 2007 - for the purpose of the accreditation, Autumn 1999, Autumn 2010 - only for the accreditation, Autumn 2000, Autumn 2002, Autumn 2003, Autumn 2004, Autumn 2005, Autumn 2006, Autumn 2007, Autumn 2008, Autumn 2009, Autumn 2010, Autumn 2011, Autumn 2011 - acreditation, Autumn 2012, Autumn 2013, Autumn 2014, Autumn 2016, autumn 2017, Autumn 2018, Autumn 2019, Autumn 2020, autumn 2021, Autumn 2022, Autumn 2023, Autumn 2024.

M5170 Mathematical Programming

Faculty of Science
Autumn 2014
Extent and Intensity
2/1/0. 3 credit(s) (příf plus uk k 1 zk 2 plus 1 > 4). Type of Completion: zk (examination).
Teacher(s)
doc. Mgr. Petr Zemánek, Ph.D. (lecturer)
Guaranteed by
prof. RNDr. Ondřej Došlý, DrSc.
Department of Mathematics and Statistics – Departments – Faculty of Science
Supplier department: Department of Mathematics and Statistics – Departments – Faculty of Science
Timetable
Fri 10:00–11:50 M1,01017
  • Timetable of Seminar Groups:
M5170/01: Fri 12:00–12:50 M1,01017, P. Zemánek
Course Enrolment Limitations
The course is also offered to the students of the fields other than those the course is directly associated with.
fields of study / plans the course is directly associated with
Course objectives
After passing the course, the student will be able:
to define and interpret the basic notions used in the basic parts of convex analysis and to explain their mutual context;
to formulate relevant mathematical theorems and statements and to explain methods of their proofs;
to use effective techniques utilized in basic fields of convex analysis;
to apply acquired pieces of knowledge for the solution of specific problems of convex programming and to some numerical methods of optimization including problems of applicative character.
Syllabus
  • I. Convex analysis. Convex sets: basic concepts, convex hull, separation and supporting hyperplanes. Convex Functions: basic concepts, convexity criteria for differentiable functions, subgradient and subdifferential, Fenchel transformation, system of linear and convex inequalities. II. Duality, necessary and sufficient conditions for optimality. Lagrange principle, Kuhn-Tucker conditions, casic concepts of convex progamming. Duality in mathematical programming, dual problem, Kuhn-Tucker vector, saddle point. Duality in special optimization problems {linear and quadratic}. III. Numerical methods of minimization. Onedimensional minimization {Fibonaci and golden ratio methods} Unconstrained optimization (steepest slope method, method of conjugate gradients, Newton method}. Quadratic programming {Wolfe method and modofications, Thiel van de Panne method}.
Literature
  • DOŠLÝ, Ondřej. Základy konvexní analýzy a optimalizace v Rn. 1. vyd. Brno: Masarykova univerzita, 2005, viii, 185. ISBN 8021039051. info
  • HAMALA, Milan. Nelineárne programovanie. 2., dopl. vyd. Bratislava: Alfa, vydavateľstvo technickej a ekonomickej literatúry, 1976, 240 s. info
Teaching methods
Theoretical lecture and seminar
Assessment methods
The standard lecture and accompany exercise, the exam has written and oral part.
Language of instruction
Czech
Follow-Up Courses
Further comments (probably available only in Czech)
Study Materials
The course is taught annually.
The course is also listed under the following terms Autumn 2007 - for the purpose of the accreditation, Autumn 1999, Autumn 2010 - only for the accreditation, Autumn 2000, Autumn 2002, Autumn 2003, Autumn 2004, Autumn 2005, Autumn 2006, Autumn 2007, Autumn 2008, Autumn 2009, Autumn 2010, Autumn 2011, Autumn 2011 - acreditation, Autumn 2012, Autumn 2013, Autumn 2015, Autumn 2016, autumn 2017, Autumn 2018, Autumn 2019, Autumn 2020, autumn 2021, Autumn 2022, Autumn 2023, Autumn 2024.

M5170 Mathematical Programming

Faculty of Science
Autumn 2013
Extent and Intensity
2/1/0. 3 credit(s) (příf plus uk k 1 zk 2 plus 1 > 4). Type of Completion: zk (examination).
Teacher(s)
prof. RNDr. Ondřej Došlý, DrSc. (lecturer)
Guaranteed by
prof. RNDr. Ondřej Došlý, DrSc.
Department of Mathematics and Statistics – Departments – Faculty of Science
Supplier department: Department of Mathematics and Statistics – Departments – Faculty of Science
Timetable
Thu 8:00–9:50 M1,01017
  • Timetable of Seminar Groups:
M5170/01: Thu 10:00–10:50 M1,01017, O. Došlý
Course Enrolment Limitations
The course is also offered to the students of the fields other than those the course is directly associated with.
fields of study / plans the course is directly associated with
Course objectives
After passing the course, the student will be able:
to define and interpret the basic notions used in the basic parts of convex analysis and to explain their mutual context;
to formulate relevant mathematical theorems and statements and to explain methods of their proofs;
to use effective techniques utilized in basic fields of convex analysis;
to apply acquired pieces of knowledge for the solution of specific problems of convex programming and to some numerical methods of optimization including problems of applicative character.
Syllabus
  • I. Convex analysis. Convex sets: basic concepts, convex hull, separation and supporting hyperplanes. Convex Functions: basic concepts, convexity criteria for differentiable functions, subgradient and subdifferential, Fenchel transformation, system of linear and convex inequalities. II. Duality, necessary and sufficient conditions for optimality. Lagrange principle, Kuhn-Tucker conditions, casic concepts of convex progamming. Duality in mathematical programming, dual problem, Kuhn-Tucker vector, saddle point. Duality in special optimization problems {linear and quadratic}. III. Numerical methods of minimization. Onedimensional minimization {Fibonaci and golden ratio methods} Unconstrained optimization (steepest slope method, method of conjugate gradients, Newton method}. Quadratic programming {Wolfe method and modofications, Thiel van de Panne method}.
Literature
  • DOŠLÝ, Ondřej. Základy konvexní analýzy a optimalizace v Rn. 1. vyd. Brno: Masarykova univerzita, 2005, viii, 185. ISBN 8021039051. info
  • HAMALA, Milan. Nelineárne programovanie. 2., dopl. vyd. Bratislava: Alfa, vydavateľstvo technickej a ekonomickej literatúry, 1976, 240 s. info
Teaching methods
Theoretical lecture and seminar
Assessment methods
The standard lecture and accompany exercise, the exam has written and oral part.
Language of instruction
Czech
Follow-Up Courses
Further comments (probably available only in Czech)
Study Materials
The course is taught annually.
The course is also listed under the following terms Autumn 2007 - for the purpose of the accreditation, Autumn 1999, Autumn 2010 - only for the accreditation, Autumn 2000, Autumn 2002, Autumn 2003, Autumn 2004, Autumn 2005, Autumn 2006, Autumn 2007, Autumn 2008, Autumn 2009, Autumn 2010, Autumn 2011, Autumn 2011 - acreditation, Autumn 2012, Autumn 2014, Autumn 2015, Autumn 2016, autumn 2017, Autumn 2018, Autumn 2019, Autumn 2020, autumn 2021, Autumn 2022, Autumn 2023, Autumn 2024.

M5170 Mathematical Programming

Faculty of Science
Autumn 2012
Extent and Intensity
2/1/0. 3 credit(s) (příf plus uk k 1 zk 2 plus 1 > 4). Type of Completion: zk (examination).
Teacher(s)
prof. RNDr. Ondřej Došlý, DrSc. (lecturer)
Guaranteed by
prof. RNDr. Ondřej Došlý, DrSc.
Department of Mathematics and Statistics – Departments – Faculty of Science
Supplier department: Department of Mathematics and Statistics – Departments – Faculty of Science
Timetable
Mon 8:00–9:50 M1,01017
  • Timetable of Seminar Groups:
M5170/01: Mon 10:00–10:50 M1,01017, O. Došlý
Course Enrolment Limitations
The course is also offered to the students of the fields other than those the course is directly associated with.
fields of study / plans the course is directly associated with
Course objectives
The aim of the course is to provide the basic fact of the convex and its application to optimization problems in finitedimensonal spaces. A particular attention is devoted to the convex programming and to some numerical methods of optimization.
Syllabus
  • I. Convex analysis. Convex sets: basic concepts, convex hull, separation and supporting hyperplanes. Convex Functions: basic concepts, convexity criteria for differentiable functions, subgradient and subdifferential, Fenchel transformation, system of linear and convex inequalities. II. Duality, necessary and sufficient conditions for optimality. Lagrange principle, Kuhn-Tucker conditions, casic concepts of convex progamming. Duality in mathematical programming, dual problem, Kuhn-Tucker vrctor, saddle point. Duality in special optimization problems {linear and quadratic}. III. Numerical methods of minimization. Onedimensional minimization {Fibonaci and golden ratio methods} Unconstrained optimization (steepest slope method, method of conjugate gradients, Newton method}. Quadratic programming {Wolfe method and modofications, Thiel van de Panne method}.
Literature
  • DOŠLÝ, Ondřej. Základy konvexní analýzy a optimalizace v Rn. 1. vyd. Brno: Masarykova univerzita, 2005, viii, 185. ISBN 8021039051. info
  • HAMALA, Milan. Nelineárne programovanie. 2., dopl. vyd. Bratislava: Alfa, vydavateľstvo technickej a ekonomickej literatúry, 1976, 240 s. info
Teaching methods
Theoretical lecture and seminar
Assessment methods
The standard lecture and accompany exercise, the exam has written and oral part.
Language of instruction
Czech
Follow-Up Courses
Further comments (probably available only in Czech)
Study Materials
The course is taught annually.
The course is also listed under the following terms Autumn 2007 - for the purpose of the accreditation, Autumn 1999, Autumn 2010 - only for the accreditation, Autumn 2000, Autumn 2002, Autumn 2003, Autumn 2004, Autumn 2005, Autumn 2006, Autumn 2007, Autumn 2008, Autumn 2009, Autumn 2010, Autumn 2011, Autumn 2011 - acreditation, Autumn 2013, Autumn 2014, Autumn 2015, Autumn 2016, autumn 2017, Autumn 2018, Autumn 2019, Autumn 2020, autumn 2021, Autumn 2022, Autumn 2023, Autumn 2024.

M5170 Mathematical Programming

Faculty of Science
Autumn 2011
Extent and Intensity
2/1/0. 3 credit(s) (příf plus uk k 1 zk 2 plus 1 > 4). Type of Completion: zk (examination).
Teacher(s)
prof. RNDr. Ondřej Došlý, DrSc. (lecturer)
Guaranteed by
prof. RNDr. Ondřej Došlý, DrSc.
Department of Mathematics and Statistics – Departments – Faculty of Science
Timetable
Fri 10:00–11:50 M1,01017
  • Timetable of Seminar Groups:
M5170/01: Fri 12:00–12:50 M1,01017, O. Došlý
Prerequisites (in Czech)
KREDITY_MIN(30)
Course Enrolment Limitations
The course is also offered to the students of the fields other than those the course is directly associated with.
fields of study / plans the course is directly associated with
Course objectives
The aim of the course is to provide the basic fact of the convex and its application to optimization problems in finitedimensonal spaces. A particular attention is devoted to the convex programming and to some numerical methods of optimization.
Syllabus
  • I. Convex analysis. Convex sets: basic concepts, convex hull, separation and supporting hyperplanes. Convex Functions: basic concepts, convexity criteria for differentiable functions, subgradient and subdifferential, Fenchel transformation, system of linear and convex inequalities. II. Duality, necessary and sufficient conditions for optimality. Lagrange principle, Kuhn-Tucker conditions, casic concepts of convex progamming. Duality in mathematical programming, dual problem, Kuhn-Tucker vrctor, saddle point. Duality in special optimization problems {linear and quadratic}. III. Numerical methods of minimization. Onedimensional minimization {Fibonaci and golden ratio methods} Unconstrained optimization (steepest slope method, method of conjugate gradients, Newton method}. Quadratic programming {Wolfe method and modofications, Thiel van de Panne method}.
Literature
  • DOŠLÝ, Ondřej. Základy konvexní analýzy a optimalizace v Rn. 1. vyd. Brno: Masarykova univerzita, 2005, viii, 185. ISBN 8021039051. info
  • HAMALA, Milan. Nelineárne programovanie. 2., dopl. vyd. Bratislava: Alfa, vydavateľstvo technickej a ekonomickej literatúry, 1976, 240 s. info
Teaching methods
Theoretical lecture and seminar
Assessment methods
The standard lecture and accompany exercise, the exam has written and oral part.
Language of instruction
Czech
Follow-Up Courses
Further comments (probably available only in Czech)
Study Materials
The course is taught annually.
The course is also listed under the following terms Autumn 2007 - for the purpose of the accreditation, Autumn 1999, Autumn 2010 - only for the accreditation, Autumn 2000, Autumn 2002, Autumn 2003, Autumn 2004, Autumn 2005, Autumn 2006, Autumn 2007, Autumn 2008, Autumn 2009, Autumn 2010, Autumn 2011 - acreditation, Autumn 2012, Autumn 2013, Autumn 2014, Autumn 2015, Autumn 2016, autumn 2017, Autumn 2018, Autumn 2019, Autumn 2020, autumn 2021, Autumn 2022, Autumn 2023, Autumn 2024.

M5170 Mathematical Programming

Faculty of Science
Autumn 2010
Extent and Intensity
2/1/0. 3 credit(s) (příf plus uk k 1 zk 2 plus 1 > 4). Type of Completion: zk (examination).
Teacher(s)
prof. RNDr. Ondřej Došlý, DrSc. (lecturer)
Guaranteed by
prof. RNDr. Ondřej Došlý, DrSc.
Department of Mathematics and Statistics – Departments – Faculty of Science
Timetable
Fri 10:00–11:50 M1,01017
  • Timetable of Seminar Groups:
M5170/01: Fri 12:00–12:50 M1,01017, O. Došlý
Prerequisites (in Czech)
KREDITY_MIN(30)
Course Enrolment Limitations
The course is also offered to the students of the fields other than those the course is directly associated with.
fields of study / plans the course is directly associated with
Course objectives
The aim of the course is to provide the basic fact of the convex and its application to optimization problems in finitedimensonal spaces. A particular attention is devoted to the convex programming and to some numerical methods of optimization.
Syllabus
  • I. Convex analysis. Convex sets: basic concepts, convex hull, separation and supporting hyperplanes. Convex Functions: basic concepts, convexity criteria for differentiable functions, subgradient and subdifferential, Fenchel transformation, system of linear and convex inequalities. II. Duality, necessary and sufficient conditions for optimality. Lagrange principle, Kuhn-Tucker conditions, casic concepts of convex progamming. Duality in mathematical programming, dual problem, Kuhn-Tucker vrctor, saddle point. Duality in special optimization problems {linear and quadratic}. III. Numerical methods of minimization. Onedimensional minimization {Fibonaci and golden ratio methods} Unconstrained optimization (steepest slope method, method of conjugate gradients, Newton method}. Quadratic programming {Wolfe method and modofications, Thiel van de Panne method}.
Literature
  • DOŠLÝ, Ondřej. Základy konvexní analýzy a optimalizace v Rn. 1. vyd. Brno: Masarykova univerzita, 2005, viii, 185. ISBN 8021039051. info
  • HAMALA, Milan. Nelineárne programovanie. 2., dopl. vyd. Bratislava: Alfa, vydavateľstvo technickej a ekonomickej literatúry, 1976, 240 s. info
Teaching methods
Theoretical lecture and seminar
Assessment methods
The standard lecture and accompany exercise, the exam has written and oral part.
Language of instruction
Czech
Follow-Up Courses
Further comments (probably available only in Czech)
Study Materials
The course is taught annually.
The course is also listed under the following terms Autumn 2007 - for the purpose of the accreditation, Autumn 1999, Autumn 2010 - only for the accreditation, Autumn 2000, Autumn 2002, Autumn 2003, Autumn 2004, Autumn 2005, Autumn 2006, Autumn 2007, Autumn 2008, Autumn 2009, Autumn 2011, Autumn 2011 - acreditation, Autumn 2012, Autumn 2013, Autumn 2014, Autumn 2015, Autumn 2016, autumn 2017, Autumn 2018, Autumn 2019, Autumn 2020, autumn 2021, Autumn 2022, Autumn 2023, Autumn 2024.

M5170 Mathematical Programming

Faculty of Science
Autumn 2009
Extent and Intensity
2/1/0. 3 credit(s) (fasci plus compl plus > 4). Type of Completion: zk (examination).
Teacher(s)
prof. RNDr. Ondřej Došlý, DrSc. (lecturer)
prof. RNDr. Roman Šimon Hilscher, DSc. (alternate examiner)
Guaranteed by
prof. RNDr. Ondřej Došlý, DrSc.
Department of Mathematics and Statistics – Departments – Faculty of Science
Timetable
Thu 12:00–13:50 M1,01017
  • Timetable of Seminar Groups:
M5170/01: Thu 14:00–14:50 M1,01017, O. Došlý
Course Enrolment Limitations
The course is also offered to the students of the fields other than those the course is directly associated with.
fields of study / plans the course is directly associated with
Course objectives
The aim of the course is to provide the basic fact of the convex and its application to optimization problems in finitedimensonal spaces. A particular attention is devoted to the convex programming and to some numerical methods of optimization.
Syllabus
  • I. Convex analysis. Convex sets: basic concepts, convex hull, separation and supporting hyperplanes. Convex Functions: basic concepts, convexity criteria for differentiable functions, subgradient and subdifferential, Fenchel transformation, system of linear and convex inequalities. II. Duality, necessary and sufficient conditions for optimality. Lagrange principle, Kuhn-Tucker conditions, casic concepts of convex progamming. Duality in mathematical programming, dual problem, Kuhn-Tucker vrctor, saddle point. Duality in special optimization problems {linear and quadratic}. III. Numerical methods of minimization. Onedimensional minimization {Fibonaci and golden ratio methods} Unconstrained optimization (steepest slope method, method of conjugate gradients, Newton method}. Quadratic programming {Wolfe method and modofications, Thiel van de Panne method}.
Literature
  • DOŠLÝ, Ondřej. Základy konvexní analýzy a optimalizace v Rn. 1. vyd. Brno: Masarykova univerzita, 2005, viii, 185. ISBN 8021039051. info
  • HAMALA, Milan. Nelineárne programovanie. 2., dopl. vyd. Bratislava: Alfa, vydavateľstvo technickej a ekonomickej literatúry, 1976, 240 s. info
Teaching methods
Theoretical lecture and seminar
Assessment methods
The standard lecture and accompany exercise, the exam has written and oral part.
Language of instruction
Czech
Follow-Up Courses
Further comments (probably available only in Czech)
Study Materials
The course is taught annually.
The course is also listed under the following terms Autumn 2007 - for the purpose of the accreditation, Autumn 1999, Autumn 2010 - only for the accreditation, Autumn 2000, Autumn 2002, Autumn 2003, Autumn 2004, Autumn 2005, Autumn 2006, Autumn 2007, Autumn 2008, Autumn 2010, Autumn 2011, Autumn 2011 - acreditation, Autumn 2012, Autumn 2013, Autumn 2014, Autumn 2015, Autumn 2016, autumn 2017, Autumn 2018, Autumn 2019, Autumn 2020, autumn 2021, Autumn 2022, Autumn 2023, Autumn 2024.

M5170 Mathematical Programming

Faculty of Science
Autumn 2008
Extent and Intensity
2/1/0. 3 credit(s) (fasci plus compl plus > 4). Type of Completion: zk (examination).
Teacher(s)
prof. RNDr. Ondřej Došlý, DrSc. (lecturer)
Guaranteed by
prof. RNDr. Ondřej Došlý, DrSc.
Department of Mathematics and Statistics – Departments – Faculty of Science
Timetable
Tue 15:00–16:50 M1,01017
  • Timetable of Seminar Groups:
M5170/01: Tue 17:00–17:50 M1,01017, O. Došlý
Course Enrolment Limitations
The course is also offered to the students of the fields other than those the course is directly associated with.
fields of study / plans the course is directly associated with
Course objectives
The aim of the course is to provide the basic fact of the convex and its application to optimization problems in finitedimensonal spaces. A particular attention is devoted to the convex programming and to numerical some methods of optimization.
Syllabus
  • I. Convex analysis. Convex sets: basic concepts, convex hull, separation and supporting hyperplanes. Convex Functions: basic concepts, convexity criteria for differentiable functions, subgradient and subdifferential, Fenchel transformation, system of linear and convex inequalities. II. Duality, necessary and sufficient conditions for optimality. Lagrange principle, Kuhn-Tucker conditions, casic concepts of convex progamming. Duality in mathematical programming, dual problem, Kuhn-Tucker vrctor, saddle point. Duality in special optimization problems {linear and quadratic}. III. Numerical methods of minimization. Onedimensional minimization {Fibonaci and golden ratio methods} Unconstrained optimization (steepest slope method, method of conjugate gradients, Newton method}. Quadratic programming {Wolfe method and modofications, Thiel van de Panne method}.
Literature
  • DOŠLÝ, Ondřej. Základy konvexní analýzy a optimalizace v Rn. 1. vyd. Brno: Masarykova univerzita, 2005, viii, 185. ISBN 8021039051. info
  • HAMALA, Milan. Nelineárne programovanie. 2., dopl. vyd. Bratislava: Alfa, vydavateľstvo technickej a ekonomickej literatúry, 1976, 240 s. info
Assessment methods
The standard lecture and accompany exercise, the exam has written and oral part.
Language of instruction
Czech
Follow-Up Courses
Further comments (probably available only in Czech)
The course is taught annually.
The course is also listed under the following terms Autumn 2007 - for the purpose of the accreditation, Autumn 1999, Autumn 2010 - only for the accreditation, Autumn 2000, Autumn 2002, Autumn 2003, Autumn 2004, Autumn 2005, Autumn 2006, Autumn 2007, Autumn 2009, Autumn 2010, Autumn 2011, Autumn 2011 - acreditation, Autumn 2012, Autumn 2013, Autumn 2014, Autumn 2015, Autumn 2016, autumn 2017, Autumn 2018, Autumn 2019, Autumn 2020, autumn 2021, Autumn 2022, Autumn 2023, Autumn 2024.

M5170 Mathematical Programming

Faculty of Science
Autumn 2007
Extent and Intensity
2/1/0. 3 credit(s) (fasci plus compl plus > 4). Type of Completion: zk (examination).
Teacher(s)
prof. RNDr. Ondřej Došlý, DrSc. (lecturer)
Guaranteed by
prof. RNDr. Ondřej Došlý, DrSc.
Department of Mathematics and Statistics – Departments – Faculty of Science
Timetable
Tue 16:00–17:50 N41
  • Timetable of Seminar Groups:
M5170/01: Tue 18:00–18:50 N41, O. Došlý
Course Enrolment Limitations
The course is also offered to the students of the fields other than those the course is directly associated with.
fields of study / plans the course is directly associated with
Course objectives
The aim of the course is to provide the basic fact of the convex and its application to optimization problems in finitedimensonal spaces. A particular attention is devoted to the convex programming and to numerical some methods of optimization.
Syllabus
  • I. Convex analysis. Convex sets: basic concepts, convex hull, separation and supporting hyperplanes. Convex Functions: basic concepts, convexity criteria for differentiable functions, subgradient and subdifferential, Fenchel transformation, system of linear and convex inequalities. II. Duality, necessary and sufficient conditions for optimality. Lagrange principle, Kuhn-Tucker conditions, casic concepts of convex progamming. Duality in mathematical programming, dual problem, Kuhn-Tucker vrctor, saddle point. Duality in special optimization problems {linear and quadratic}. III. Numerical methods of minimization. Onedimensional minimization {Fibonaci and golden ratio methods} Unconstrained optimization (steepest slope method, method of conjugate gradients, Newton method}. Quadratic programming {Wolfe method and modofications, Thiel van de Panne method}.
Literature
  • DOŠLÝ, Ondřej. Základy konvexní analýzy a optimalizace v Rn. 1. vyd. Brno: Masarykova univerzita, 2005, viii, 185. ISBN 8021039051. info
  • HAMALA, Milan. Nelineárne programovanie. 2., dopl. vyd. Bratislava: Alfa, vydavateľstvo technickej a ekonomickej literatúry, 1976, 240 s. info
Assessment methods (in Czech)
Standardní přednáška a cvičení, zkouška má písemnou i ústní část.
Language of instruction
Czech
Follow-Up Courses
Further comments (probably available only in Czech)
The course is taught annually.
The course is also listed under the following terms Autumn 2007 - for the purpose of the accreditation, Autumn 1999, Autumn 2010 - only for the accreditation, Autumn 2000, Autumn 2002, Autumn 2003, Autumn 2004, Autumn 2005, Autumn 2006, Autumn 2008, Autumn 2009, Autumn 2010, Autumn 2011, Autumn 2011 - acreditation, Autumn 2012, Autumn 2013, Autumn 2014, Autumn 2015, Autumn 2016, autumn 2017, Autumn 2018, Autumn 2019, Autumn 2020, autumn 2021, Autumn 2022, Autumn 2023, Autumn 2024.

M5170 Mathematical Programming

Faculty of Science
Autumn 2006
Extent and Intensity
2/1/0. 3 credit(s) (fasci plus compl plus > 4). Type of Completion: zk (examination).
Teacher(s)
prof. RNDr. Ondřej Došlý, DrSc. (lecturer)
Guaranteed by
prof. RNDr. Ondřej Došlý, DrSc.
Department of Mathematics and Statistics – Departments – Faculty of Science
Contact Person: prof. RNDr. Ondřej Došlý, DrSc.
Timetable
Fri 10:00–11:50 N41
  • Timetable of Seminar Groups:
M5170/01: Fri 12:00–12:50 N41
Prerequisites (in Czech)
M4110 Linear programming
Course Enrolment Limitations
The course is also offered to the students of the fields other than those the course is directly associated with.
fields of study / plans the course is directly associated with
Course objectives
The aim of the course is to provide the basic fact of the convex and its application to optimization problems in finitedimensonal spaces. A particular attention is devoted to the convex programming and to numerical some methods of optimization.
Syllabus
  • I. Convex analysis. Convex sets: basic concepts, convex hull, separation and supporting hyperplanes. Convex Functions: basic concepts, convexity criteria for differentiable functions, subgradient and subdifferential, Fenchel transformation, system of linear and convex inequalities. II. Duality, necessary and sufficient conditions for optimality. Lagrange principle, Kuhn-Tucker conditions, casic concepts of convex progamming. Duality in mathematical programming, dual problem, Kuhn-Tucker vrctor, saddle point. Duality in special optimization problems {linear and quadratic}. III. Numerical methods of minimization. Onedimensional minimization {Fibonaci and golden ratio methods} Unconstrained optimization (steepest slope method, method of conjugate gradients, Newton method}. Quadratic programming {Wolfe method and modofications, Thiel van de Panne method}.
Literature
  • DOŠLÝ, Ondřej. Základy konvexní analýzy a optimalizace v Rn. 1. vyd. Brno: Masarykova univerzita, 2005, viii, 185. ISBN 8021039051. info
  • HAMALA, Milan. Nelineárne programovanie. 2., dopl. vyd. Bratislava: Alfa, vydavateľstvo technickej a ekonomickej literatúry, 1976, 240 s. info
Assessment methods (in Czech)
Standardní přednáška a cvičení, zkouška má písemnou i ústní část.
Language of instruction
Czech
Follow-Up Courses
Further comments (probably available only in Czech)
The course can also be completed outside the examination period.
The course is taught annually.
The course is also listed under the following terms Autumn 2007 - for the purpose of the accreditation, Autumn 1999, Autumn 2010 - only for the accreditation, Autumn 2000, Autumn 2002, Autumn 2003, Autumn 2004, Autumn 2005, Autumn 2007, Autumn 2008, Autumn 2009, Autumn 2010, Autumn 2011, Autumn 2011 - acreditation, Autumn 2012, Autumn 2013, Autumn 2014, Autumn 2015, Autumn 2016, autumn 2017, Autumn 2018, Autumn 2019, Autumn 2020, autumn 2021, Autumn 2022, Autumn 2023, Autumn 2024.

M5170 Mathematical Programming

Faculty of Science
Autumn 2005
Extent and Intensity
2/1/0. 3 credit(s) (fasci plus compl plus > 4). Type of Completion: zk (examination).
Teacher(s)
prof. RNDr. Ondřej Došlý, DrSc. (lecturer)
Guaranteed by
prof. RNDr. Ondřej Došlý, DrSc.
Department of Mathematics and Statistics – Departments – Faculty of Science
Contact Person: prof. RNDr. Ondřej Došlý, DrSc.
Timetable
Fri 10:00–11:50 N21
  • Timetable of Seminar Groups:
M5170/01: Fri 12:00–12:50 N21, O. Došlý
Prerequisites (in Czech)
M4110 Linear programming
Course Enrolment Limitations
The course is also offered to the students of the fields other than those the course is directly associated with.
fields of study / plans the course is directly associated with
Course objectives
The aim of the course is to provide the basic fact of the convex and its application to optimization problems in finitedimensonal spaces. A particular attention is devoted to the convex programming and to numerical some methods of optimization.
Syllabus
  • I. Convex analysis. Convex sets: basic concepts, convex hull, separation and supporting hyperplanes. Convex Functions: basic concepts, convexity criteria for differentiable functions, subgradient and subdifferential, Fenchel transformation, system of linear and convex inequalities. II. Duality, necessary and sufficient conditions for optimality. Lagrange principle, Kuhn-Tucker conditions, casic concepts of convex progamming. Duality in mathematical programming, dual problem, Kuhn-Tucker vrctor, saddle point. Duality in special optimization problems {linear and quadratic}. III. Numerical methods of minimization. Onedimensional minimization {Fibonaci and golden ratio methods} Unconstrained optimization (steepest slope method, method of conjugate gradients, Newton method}. Quadratic programming {Wolfe method and modofications, Thiel van de Panne method}.
Literature
  • SUCHAREV, Aleksej Grigor‘jevič, Aleksandr Vasil'jevič TIMOCHOV and Vjačeslav Vasil'jevič FEDOROV. Kurs metodov optimizacii. Moskva: Nauka, 1986, 325 s. info
  • HAMALA, Milan. Nelineárne programovanie. 2., dopl. vyd. Bratislava: Alfa, vydavateľstvo technickej a ekonomickej literatúry, 1976, 240 s. info
Assessment methods (in Czech)
Standardní přednáška a cvičení, zkouška má písemnou i ústní část.
Language of instruction
Czech
Follow-Up Courses
Further comments (probably available only in Czech)
The course can also be completed outside the examination period.
The course is taught annually.
The course is also listed under the following terms Autumn 2007 - for the purpose of the accreditation, Autumn 1999, Autumn 2010 - only for the accreditation, Autumn 2000, Autumn 2002, Autumn 2003, Autumn 2004, Autumn 2006, Autumn 2007, Autumn 2008, Autumn 2009, Autumn 2010, Autumn 2011, Autumn 2011 - acreditation, Autumn 2012, Autumn 2013, Autumn 2014, Autumn 2015, Autumn 2016, autumn 2017, Autumn 2018, Autumn 2019, Autumn 2020, autumn 2021, Autumn 2022, Autumn 2023, Autumn 2024.

M5170 Mathematical Programming

Faculty of Science
Autumn 2004
Extent and Intensity
2/1/0. 3 credit(s) (fasci plus compl plus > 4). Type of Completion: zk (examination).
Teacher(s)
prof. RNDr. Ondřej Došlý, DrSc. (lecturer)
Guaranteed by
prof. RNDr. Ondřej Došlý, DrSc.
Department of Mathematics and Statistics – Departments – Faculty of Science
Contact Person: prof. RNDr. Ondřej Došlý, DrSc.
Timetable
Tue 12:00–13:50 UP1
  • Timetable of Seminar Groups:
M5170/01: Tue 14:00–14:50 UP1, O. Došlý
Prerequisites (in Czech)
M4110 Linear programming
Course Enrolment Limitations
The course is also offered to the students of the fields other than those the course is directly associated with.
fields of study / plans the course is directly associated with
there are 6 fields of study the course is directly associated with, display
Course objectives
The aim of the course is to provide the basic fact of the convex and its application to optimization problems in finitedimensonal spaces. A particular attention is devoted to the convex programming and to numerical some methods of optimization.
Syllabus
  • I. Convex analysis. Convex sets: basic concepts, convex hull, separation and supporting hyperplanes. Convex Functions: basic concepts, convexity criteria for differentiable functions, subgradient and subdifferential, Fenchel transformation, system of linear and convex inequalities. II. Duality, necessary and sufficient conditions for optimality. Lagrange principle, Kuhn-Tucker conditions, casic concepts of convex progamming. Duality in mathematical programming, dual problem, Kuhn-Tucker vrctor, saddle point. Duality in special optimization problems {linear and quadratic}. III. Numerical methods of minimization. Onedimensional minimization {Fibonaci and golden ratio methods} Unconstrained optimization (steepest slope method, method of conjugate gradients, Newton method}. Quadratic programming {Wolfe method and modofications, Thiel van de Panne method}.
Literature
  • SUCHAREV, Aleksej Grigor‘jevič, Aleksandr Vasil'jevič TIMOCHOV and Vjačeslav Vasil'jevič FEDOROV. Kurs metodov optimizacii. Moskva: Nauka, 1986, 325 s. info
  • HAMALA, Milan. Nelineárne programovanie. 2., dopl. vyd. Bratislava: Alfa, vydavateľstvo technickej a ekonomickej literatúry, 1976, 240 s. info
Assessment methods (in Czech)
Standardní přednáška a cvičení, zkouška má písemnou i ústní část.
Language of instruction
Czech
Follow-Up Courses
Further comments (probably available only in Czech)
The course can also be completed outside the examination period.
The course is taught annually.
The course is also listed under the following terms Autumn 2007 - for the purpose of the accreditation, Autumn 1999, Autumn 2010 - only for the accreditation, Autumn 2000, Autumn 2002, Autumn 2003, Autumn 2005, Autumn 2006, Autumn 2007, Autumn 2008, Autumn 2009, Autumn 2010, Autumn 2011, Autumn 2011 - acreditation, Autumn 2012, Autumn 2013, Autumn 2014, Autumn 2015, Autumn 2016, autumn 2017, Autumn 2018, Autumn 2019, Autumn 2020, autumn 2021, Autumn 2022, Autumn 2023, Autumn 2024.

M5170 Mathematical Programming

Faculty of Science
Autumn 2003
Extent and Intensity
2/1/0. 3 credit(s) (fasci plus compl plus > 4). Type of Completion: zk (examination).
Teacher(s)
prof. RNDr. Ondřej Došlý, DrSc. (lecturer)
Guaranteed by
prof. RNDr. Ondřej Došlý, DrSc.
Department of Mathematics and Statistics – Departments – Faculty of Science
Contact Person: prof. RNDr. Ondřej Došlý, DrSc.
Timetable of Seminar Groups
M5170/01: No timetable has been entered into IS. O. Došlý
Prerequisites (in Czech)
M4110 Linear programming
Course Enrolment Limitations
The course is also offered to the students of the fields other than those the course is directly associated with.
fields of study / plans the course is directly associated with
Course objectives
The aim of the course is to provide the basic fact of the convex and its application to optimization problems in finitedimensonal spaces. A particular attention is devoted to the convex programming and to numerical some methods of optimization.
Syllabus
  • I. Convex analysis. Convex sets: basic concepts, convex hull, separation and supporting hyperplanes. Convex Functions: basic concepts, convexity criteria for differentiable functions, subgradient and subdifferential, Fenchel transformation, system of linear and convex inequalities. II. Duality, necessary and sufficient conditions for optimality. Lagrange principle, Kuhn-Tucker conditions, casic concepts of convex progamming. Duality in mathematical programming, dual problem, Kuhn-Tucker vrctor, saddle point. Duality in special optimization problems {linear and quadratic}. III. Numerical methods of minimization. Onedimensional minimization {Fibonaci and golden ratio methods} Unconstrained optimization (steepest slope method, method of conjugate gradients, Newton method}. Quadratic programming {Wolfe method and modofications, Thiel van de Panne method}.
Literature
  • SUCHAREV, Aleksej Grigor‘jevič, Aleksandr Vasil'jevič TIMOCHOV and Vjačeslav Vasil'jevič FEDOROV. Kurs metodov optimizacii. Moskva: Nauka, 1986, 325 s. info
  • HAMALA, Milan. Nelineárne programovanie. 2., dopl. vyd. Bratislava: Alfa, vydavateľstvo technickej a ekonomickej literatúry, 1976, 240 s. info
Assessment methods (in Czech)
Standardní přednáška a cvičení, zkouška má písemnou i ústní část.
Language of instruction
Czech
Follow-Up Courses
Further comments (probably available only in Czech)
The course can also be completed outside the examination period.
The course is taught annually.
The course is also listed under the following terms Autumn 2007 - for the purpose of the accreditation, Autumn 1999, Autumn 2010 - only for the accreditation, Autumn 2000, Autumn 2002, Autumn 2004, Autumn 2005, Autumn 2006, Autumn 2007, Autumn 2008, Autumn 2009, Autumn 2010, Autumn 2011, Autumn 2011 - acreditation, Autumn 2012, Autumn 2013, Autumn 2014, Autumn 2015, Autumn 2016, autumn 2017, Autumn 2018, Autumn 2019, Autumn 2020, autumn 2021, Autumn 2022, Autumn 2023, Autumn 2024.

M5170 Mathematical Programming

Faculty of Science
Autumn 2002
Extent and Intensity
2/1/0. 3 credit(s) (fasci plus compl plus > 4). Type of Completion: zk (examination).
Teacher(s)
prof. RNDr. Ondřej Došlý, DrSc. (lecturer)
Guaranteed by
prof. RNDr. Ondřej Došlý, DrSc.
Department of Mathematics and Statistics – Departments – Faculty of Science
Contact Person: prof. RNDr. Ondřej Došlý, DrSc.
Prerequisites (in Czech)
M4110 Linear programming
Course Enrolment Limitations
The course is also offered to the students of the fields other than those the course is directly associated with.
fields of study / plans the course is directly associated with
Course objectives
The aim of the course is to provide the basic fact of the convex and its application to optimization problems in finitedimensonal spaces. A particular attention is devoted to the convex programming and to numerical some methods of optimization.
Syllabus
  • I. Convex analysis. Convex sets: basic concepts, convex hull, separation and supporting hyperplanes. Convex Functions: basic concepts, convexity criteria for differentiable functions, subgradient and subdifferential, Fenchel transformation, system of linear and convex inequalities. II. Duality, necessary and sufficient conditions for optimality. Lagrange principle, Kuhn-Tucker conditions, casic concepts of convex progamming. Duality in mathematical programming, dual problem, Kuhn-Tucker vrctor, saddle point. Duality in special optimization problems {linear and quadratic}. III. Numerical methods of minimization. Onedimensional minimization {Fibonaci and golden ratio methods} Unconstrained optimization (steepest slope method, method of conjugate gradients, Newton method}. Quadratic programming {Wolfe method and modofications, Thiel van de Panne method}.
Literature
  • SUCHAREV, Aleksej Grigor‘jevič, Aleksandr Vasil'jevič TIMOCHOV and Vjačeslav Vasil'jevič FEDOROV. Kurs metodov optimizacii. Moskva: Nauka, 1986, 325 s. info
  • HAMALA, Milan. Nelineárne programovanie. 2., dopl. vyd. Bratislava: Alfa, vydavateľstvo technickej a ekonomickej literatúry, 1976, 240 s. info
Assessment methods (in Czech)
Standardní přednáška a cvičení, zkouška má písemnou i ústní část.
Language of instruction
Czech
Follow-Up Courses
Further comments (probably available only in Czech)
The course can also be completed outside the examination period.
The course is taught annually.
The course is taught: every week.
The course is also listed under the following terms Autumn 2007 - for the purpose of the accreditation, Autumn 1999, Autumn 2010 - only for the accreditation, Autumn 2000, Autumn 2003, Autumn 2004, Autumn 2005, Autumn 2006, Autumn 2007, Autumn 2008, Autumn 2009, Autumn 2010, Autumn 2011, Autumn 2011 - acreditation, Autumn 2012, Autumn 2013, Autumn 2014, Autumn 2015, Autumn 2016, autumn 2017, Autumn 2018, Autumn 2019, Autumn 2020, autumn 2021, Autumn 2022, Autumn 2023, Autumn 2024.

M5170 Complex Analysis

Faculty of Science
Autumn 2000
Extent and Intensity
4/2/0. 9 credit(s). Type of Completion: zk (examination).
Teacher(s)
doc. RNDr. Josef Kalas, CSc. (lecturer)
Guaranteed by
doc. RNDr. Josef Kalas, CSc.
Department of Mathematics and Statistics – Departments – Faculty of Science
Contact Person: doc. RNDr. Josef Kalas, CSc.
Prerequisites (in Czech)
M3100 Mathematical Analysis III
Course Enrolment Limitations
The course is also offered to the students of the fields other than those the course is directly associated with.
fields of study / plans the course is directly associated with
Course objectives
Topological concepts. Complex functions, complex differentiability, Cauchy-Riemann equations. Complex integration. Cauchy's theorem, Cauchy's integral formula. Fundamental properties of holomorphic functions. Power and Laurent series. Isolated singularities, calculus of residues and its applications. Entire functions. Meromorphic functions. Basic principles of conformal mapping theory.
Literature
  • ČERNÝ, Ilja. Analýza v komplexním oboru. 1. vyd. Praha: Academia, 1983, 822 s. info
  • NOVÁK, Vítězslav. Analýza v komplexním oboru. 1. vyd. Praha: Státní pedagogické nakladatelství, 1984, 103 s. info
  • Bicadze, A. V. Osnovy teorii analitičeskich funkcij komplexnogo peremennogo. nauka, Moskva, 1969.
  • JEVGRAFOV, Marat Andrejevič. Funkce komplexní proměnné. Translated by Ladislav Průcha. Vyd. 1. Praha: SNTL - Nakladatelství technické literatury, 1981, 379 s. URL info
  • JEVGRAFOV, Marat Andrejevič. Sbírka úloh z teorie funkcí komplexní proměnné. Translated by Anna Něničková - Věra Maňasová - Eva Nováková. Vyd. 1. Praha: SNTL - Nakladatelství technické literatury, 1976, 542 s. URL info
Language of instruction
Czech
Further Comments
The course can also be completed outside the examination period.
The course is taught annually.
The course is taught: every week.
The course is also listed under the following terms Autumn 2007 - for the purpose of the accreditation, Autumn 1999, Autumn 2010 - only for the accreditation, Autumn 2002, Autumn 2003, Autumn 2004, Autumn 2005, Autumn 2006, Autumn 2007, Autumn 2008, Autumn 2009, Autumn 2010, Autumn 2011, Autumn 2011 - acreditation, Autumn 2012, Autumn 2013, Autumn 2014, Autumn 2015, Autumn 2016, autumn 2017, Autumn 2018, Autumn 2019, Autumn 2020, autumn 2021, Autumn 2022, Autumn 2023, Autumn 2024.

M5170 Complex Analysis

Faculty of Science
Autumn 1999
Extent and Intensity
2/1/0. 9 credit(s). Type of Completion: zk (examination).
Teacher(s)
doc. RNDr. Josef Kalas, CSc. (lecturer)
Guaranteed by
doc. RNDr. Josef Kalas, CSc.
Department of Mathematics and Statistics – Departments – Faculty of Science
Contact Person: doc. RNDr. Josef Kalas, CSc.
Prerequisites (in Czech)
M3100 Mathematical Analysis III && M2110 Linear Algebra II
Course Enrolment Limitations
The course is also offered to the students of the fields other than those the course is directly associated with.
fields of study / plans the course is directly associated with
Syllabus
  • Topological concepts. Complex functions, complex differentiability, Cauchy-Riemann equations. Complex integration. Cauchy's theorem, Cauchy's integral formula. Fundamental properties of holomorphic functions. Power and Laurent series. Isolated singularities, calculus of residues and its applications. Entire functions. Meromorphic functions. Basic principles of conformal mapping theory.
Literature
  • ČERNÝ, Ilja. Analýza v komplexním oboru. 1. vyd. Praha: Academia, 1983, 822 s. info
  • NOVÁK, Vítězslav. Analýza v komplexním oboru. 1. vyd. Praha: Státní pedagogické nakladatelství, 1984, 103 s. info
  • Bicadze, A. V. Osnovy teorii analitičeskich funkcij komplexnogo peremennogo. nauka, Moskva, 1969.
  • JEVGRAFOV, Marat Andrejevič. Funkce komplexní proměnné. Translated by Ladislav Průcha. Vyd. 1. Praha: SNTL - Nakladatelství technické literatury, 1981, 379 s. URL info
  • JEVGRAFOV, Marat Andrejevič. Sbírka úloh z teorie funkcí komplexní proměnné. Translated by Anna Něničková - Věra Maňasová - Eva Nováková. Vyd. 1. Praha: SNTL - Nakladatelství technické literatury, 1976, 542 s. URL info
Language of instruction
Czech
Further Comments
The course can also be completed outside the examination period.
The course is taught annually.
The course is taught: every week.
The course is also listed under the following terms Autumn 2007 - for the purpose of the accreditation, Autumn 2010 - only for the accreditation, Autumn 2000, Autumn 2002, Autumn 2003, Autumn 2004, Autumn 2005, Autumn 2006, Autumn 2007, Autumn 2008, Autumn 2009, Autumn 2010, Autumn 2011, Autumn 2011 - acreditation, Autumn 2012, Autumn 2013, Autumn 2014, Autumn 2015, Autumn 2016, autumn 2017, Autumn 2018, Autumn 2019, Autumn 2020, autumn 2021, Autumn 2022, Autumn 2023, Autumn 2024.

M5170 Mathematical Programming

Faculty of Science
Autumn 2011 - acreditation

The information about the term Autumn 2011 - acreditation is not made public

Extent and Intensity
2/1/0. 3 credit(s) (příf plus uk k 1 zk 2 plus 1 > 4). Type of Completion: zk (examination).
Teacher(s)
prof. RNDr. Ondřej Došlý, DrSc. (lecturer)
Guaranteed by
prof. RNDr. Ondřej Došlý, DrSc.
Department of Mathematics and Statistics – Departments – Faculty of Science
Prerequisites (in Czech)
KREDITY_MIN(30)
Course Enrolment Limitations
The course is also offered to the students of the fields other than those the course is directly associated with.
fields of study / plans the course is directly associated with
Course objectives
The aim of the course is to provide the basic fact of the convex and its application to optimization problems in finitedimensonal spaces. A particular attention is devoted to the convex programming and to some numerical methods of optimization.
Syllabus
  • I. Convex analysis. Convex sets: basic concepts, convex hull, separation and supporting hyperplanes. Convex Functions: basic concepts, convexity criteria for differentiable functions, subgradient and subdifferential, Fenchel transformation, system of linear and convex inequalities. II. Duality, necessary and sufficient conditions for optimality. Lagrange principle, Kuhn-Tucker conditions, casic concepts of convex progamming. Duality in mathematical programming, dual problem, Kuhn-Tucker vrctor, saddle point. Duality in special optimization problems {linear and quadratic}. III. Numerical methods of minimization. Onedimensional minimization {Fibonaci and golden ratio methods} Unconstrained optimization (steepest slope method, method of conjugate gradients, Newton method}. Quadratic programming {Wolfe method and modofications, Thiel van de Panne method}.
Literature
  • DOŠLÝ, Ondřej. Základy konvexní analýzy a optimalizace v Rn. 1. vyd. Brno: Masarykova univerzita, 2005, viii, 185. ISBN 8021039051. info
  • HAMALA, Milan. Nelineárne programovanie. 2., dopl. vyd. Bratislava: Alfa, vydavateľstvo technickej a ekonomickej literatúry, 1976, 240 s. info
Teaching methods
Theoretical lecture and seminar
Assessment methods
The standard lecture and accompany exercise, the exam has written and oral part.
Language of instruction
Czech
Follow-Up Courses
Further comments (probably available only in Czech)
The course is taught annually.
The course is taught: every week.
The course is also listed under the following terms Autumn 2007 - for the purpose of the accreditation, Autumn 1999, Autumn 2010 - only for the accreditation, Autumn 2000, Autumn 2002, Autumn 2003, Autumn 2004, Autumn 2005, Autumn 2006, Autumn 2007, Autumn 2008, Autumn 2009, Autumn 2010, Autumn 2011, Autumn 2012, Autumn 2013, Autumn 2014, Autumn 2015, Autumn 2016, autumn 2017, Autumn 2018, Autumn 2019, Autumn 2020, autumn 2021, Autumn 2022, Autumn 2023, Autumn 2024.

M5170 Mathematical Programming

Faculty of Science
Autumn 2010 - only for the accreditation
Extent and Intensity
2/1/0. 3 credit(s) (příf plus uk k 1 zk 2 plus 1 > 4). Type of Completion: zk (examination).
Teacher(s)
prof. RNDr. Ondřej Došlý, DrSc. (lecturer)
Guaranteed by
prof. RNDr. Ondřej Došlý, DrSc.
Department of Mathematics and Statistics – Departments – Faculty of Science
Prerequisites (in Czech)
KREDITY_MIN(30)
Course Enrolment Limitations
The course is also offered to the students of the fields other than those the course is directly associated with.
fields of study / plans the course is directly associated with
Course objectives
The aim of the course is to provide the basic fact of the convex and its application to optimization problems in finitedimensonal spaces. A particular attention is devoted to the convex programming and to some numerical methods of optimization.
Syllabus
  • I. Convex analysis. Convex sets: basic concepts, convex hull, separation and supporting hyperplanes. Convex Functions: basic concepts, convexity criteria for differentiable functions, subgradient and subdifferential, Fenchel transformation, system of linear and convex inequalities. II. Duality, necessary and sufficient conditions for optimality. Lagrange principle, Kuhn-Tucker conditions, casic concepts of convex progamming. Duality in mathematical programming, dual problem, Kuhn-Tucker vrctor, saddle point. Duality in special optimization problems {linear and quadratic}. III. Numerical methods of minimization. Onedimensional minimization {Fibonaci and golden ratio methods} Unconstrained optimization (steepest slope method, method of conjugate gradients, Newton method}. Quadratic programming {Wolfe method and modofications, Thiel van de Panne method}.
Literature
  • DOŠLÝ, Ondřej. Základy konvexní analýzy a optimalizace v Rn. 1. vyd. Brno: Masarykova univerzita, 2005, viii, 185. ISBN 8021039051. info
  • HAMALA, Milan. Nelineárne programovanie. 2., dopl. vyd. Bratislava: Alfa, vydavateľstvo technickej a ekonomickej literatúry, 1976, 240 s. info
Teaching methods
Theoretical lecture and seminar
Assessment methods
The standard lecture and accompany exercise, the exam has written and oral part.
Language of instruction
Czech
Follow-Up Courses
Further comments (probably available only in Czech)
The course is taught annually.
The course is taught: every week.
The course is also listed under the following terms Autumn 2007 - for the purpose of the accreditation, Autumn 1999, Autumn 2000, Autumn 2002, Autumn 2003, Autumn 2004, Autumn 2005, Autumn 2006, Autumn 2007, Autumn 2008, Autumn 2009, Autumn 2010, Autumn 2011, Autumn 2011 - acreditation, Autumn 2012, Autumn 2013, Autumn 2014, Autumn 2015, Autumn 2016, autumn 2017, Autumn 2018, Autumn 2019, Autumn 2020, autumn 2021, Autumn 2022, Autumn 2023, Autumn 2024.

M5170 Mathematical Programming

Faculty of Science
Autumn 2007 - for the purpose of the accreditation
Extent and Intensity
2/1/0. 3 credit(s) (fasci plus compl plus > 4). Type of Completion: zk (examination).
Teacher(s)
prof. RNDr. Ondřej Došlý, DrSc. (lecturer)
Guaranteed by
prof. RNDr. Ondřej Došlý, DrSc.
Department of Mathematics and Statistics – Departments – Faculty of Science
Contact Person: prof. RNDr. Ondřej Došlý, DrSc.
Prerequisites (in Czech)
M4110 Linear programming
Course Enrolment Limitations
The course is also offered to the students of the fields other than those the course is directly associated with.
fields of study / plans the course is directly associated with
Course objectives
The aim of the course is to provide the basic fact of the convex and its application to optimization problems in finitedimensonal spaces. A particular attention is devoted to the convex programming and to numerical some methods of optimization.
Syllabus
  • I. Convex analysis. Convex sets: basic concepts, convex hull, separation and supporting hyperplanes. Convex Functions: basic concepts, convexity criteria for differentiable functions, subgradient and subdifferential, Fenchel transformation, system of linear and convex inequalities. II. Duality, necessary and sufficient conditions for optimality. Lagrange principle, Kuhn-Tucker conditions, casic concepts of convex progamming. Duality in mathematical programming, dual problem, Kuhn-Tucker vrctor, saddle point. Duality in special optimization problems {linear and quadratic}. III. Numerical methods of minimization. Onedimensional minimization {Fibonaci and golden ratio methods} Unconstrained optimization (steepest slope method, method of conjugate gradients, Newton method}. Quadratic programming {Wolfe method and modofications, Thiel van de Panne method}.
Literature
  • DOŠLÝ, Ondřej. Základy konvexní analýzy a optimalizace v Rn. 1. vyd. Brno: Masarykova univerzita, 2005, viii, 185. ISBN 8021039051. info
  • HAMALA, Milan. Nelineárne programovanie. 2., dopl. vyd. Bratislava: Alfa, vydavateľstvo technickej a ekonomickej literatúry, 1976, 240 s. info
Assessment methods (in Czech)
Standardní přednáška a cvičení, zkouška má písemnou i ústní část.
Language of instruction
Czech
Follow-Up Courses
Further comments (probably available only in Czech)
The course can also be completed outside the examination period.
The course is taught annually.
The course is taught: every week.
The course is also listed under the following terms Autumn 1999, Autumn 2010 - only for the accreditation, Autumn 2000, Autumn 2002, Autumn 2003, Autumn 2004, Autumn 2005, Autumn 2006, Autumn 2007, Autumn 2008, Autumn 2009, Autumn 2010, Autumn 2011, Autumn 2011 - acreditation, Autumn 2012, Autumn 2013, Autumn 2014, Autumn 2015, Autumn 2016, autumn 2017, Autumn 2018, Autumn 2019, Autumn 2020, autumn 2021, Autumn 2022, Autumn 2023, Autumn 2024.
  • Enrolment Statistics (recent)