Introduction to optimization

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Introduction to optimization

Code: 284265
ECTS: 5.0
Lecturers in charge: doc. dr. sc. Petar Kunštek
Lecturers: doc. dr. sc. Petar Kunštek - Exercises
Take exam: Studomat
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1. komponenta

Lecture typeTotal
Lectures 30
Exercises 15
* Load is given in academic hour (1 academic hour = 45 minutes)
Description:
COURSE AIMS AND OBJECTIVES:
Getting acquainted the analytical and numerical approach to optimization problems and the application of basic results on convex sets and functions in optimization. In particular, for linear problems, learn the basic results of the geometry of polyhedral sets, the meaning of duality theory and numerical methods: the simplex method and the interior point method.

COURSE DESCRIPTION AND SYLLABUS:
1. Convex sets. Affine and convex sets, convex cones, projection of a point to a set, separation of convex sets, separation of a point and a finitely generated cone
2. Linear programming. Simplex algorithm, initialization, degeneration, finiteness of simplex algorithm with Bland pivoting, complexity of simplex algorithm, duality in linear programming: complementarity conditions, sensitivity, dual simplex method
3. Polyhedral geometry. Farkas-Minkowski-Weyl theorem, representation of a polyhedral set, extreme points and extreme recessive directions, solvability of a linear programming problem
4. Non-linear programming. Convex functions, subdifferential, nonlinear and convex programming, Karush-Kuhn-Tucker conditions, John conditions, Farkas lemma
5. Numerical methods. Gradient methods of unconditional minimization, Nesterov accelerated gradient method, Newton method, interior point method
Literature:
  1. Geometrija linearnog programiranja, L. Čaklović, Element, 2010.
  2. Linear and nonlinear programming, D. Luenberger, Y. Ye, Springer, 2008.
  3. Introduction to linear optimization, D. Bertsimas, J. Tsitsiklis, Athena, 1997.
  4. Introductory Lectures on Convex Optimization: A Basic Course, Y. Nesterov, Kluwer, 2004.
  5. Convexity and Optimization, A.-L. Lindahl, Lecture Notes, 2015.
  6. Introduction to Nonlinear Optimization: Theory, Algorithms, and Applications with MATLAB, A. Beck, SIAM, 2014.
  7. Nonlinear Programming: Theory and Algorithms, M. S. Bazaraa, H. D. Sherali, C. M. Shetty, John Wiley, 2006.
1. semester
Izborni modul C - Znanost o podacima - Regular study - Computer Science and Mathematics
Izborni modul D - Znanstveno računanje - Regular study - Computer Science and Mathematics
Ostali izborni predmeti - Regular study - Computer Science and Mathematics

2. semester Not active
Izborni modul C - Znanost o podacima - Regular study - Computer Science and Mathematics
Izborni modul D - Znanstveno računanje - Regular study - Computer Science and Mathematics
Ostali izborni predmeti - Regular study - Computer Science and Mathematics
Consultations schedule: