====== AE4B33OPT -- Optimization (English Version) 2015-16 ======

Quick links:
[[./labs/start|labs]],
[[https://cw.felk.cvut.cz/forum/forum-1186.html|discussion board]],
[[http://cw.felk.cvut.cz/upload/|upload system]],
[[http://www.fel.cvut.cz/en/education/rozvrhy-ng.B151/public/en/predmety/12/82/p12820004.html|timetable]].


===== News =====

  * <wrap em>Fill in [[http://www.fel.cvut.cz/en/education/evaluation-survey.html|student's feedback]]!</wrap>

  * <wrap em>Exam will be at room G-205 on Tuesday Feb 2, 10pm. Please bring calculators and blank sheets. No notes allowed (both printed or hand-written).</wrap>

===== Overview =====

This course is about optimisation in finite-dimensional (Euclidean) spaces. It includes least squares problems, linear programming and convex optimisation. You will learn:

  * to recognise and formulate a problem as an optimisation problem with or without constraints
  * necessary and sufficient optimality conditions
  * fundamentals of convex analysis
  * some algorithms for solving optimisation problems.


===== Lectures =====

Lecturer: [[http://cmp.felk.cvut.cz/~werner|Tomáš Werner]]

Schedule (may change during the term):

^ # ^ date ^ topic ^ optional materials ^
| 01 |  02.10.| Matrix algebra. |
| 02 |  09.10.| Recap of parts of linear algebra. | [[https://see.stanford.edu/Course/EE263|EE263]] lect. 2-4 |
| 03 |  16.10.| Ortogonality, QR decomposition. | [[https://see.stanford.edu/Course/EE263|EE263]] lect. 4-5 |
| 04 |  23.10.| Least squares, least norm. | [[https://see.stanford.edu/Course/EE263|EE263]] lect. 5-6,9 |
| 05 |  30.10.| Quadratic functions, spectral decomposition. | [[https://see.stanford.edu/Course/EE263|EE263]] lect. 15-17 |
| 06 |  06.11.| Quadratic functions, spectral decomposition. |
| 07 |  13.11.| SVD | |
| 08 |  20.11.| Multivariate calculus. |
| 09 |  27.11.| Local extrema, free and equality-constrained. | Luenberger 7.1-7.3, 11.1-11.4 |
| 10 |  04.12.| Numerical algorithms to find free local extrema. | Luenberger 8.8 |
| 11 |  11.12.| Linear programming. Convex sets, convex polyhedra. | Luenberger 2.1-2.6 |
| 12 |  18.12.| Simplex method. | Luenberger 3.1-3.5 |
| 13 |  08.01.| Convex functions, convex optimization. | [[http://www.stanford.edu/class/ee364a|EE364a]] lect. 3-4 |
| 14 |  15.01.| Duality. | [[http://www.stanford.edu/class/ee364a|EE364a]] lect. 5 |

Here are {{opt.pdf|lecture notes}} (partially translated into English, the translation will gradually progress).

"Luenberger" refers to the book [[https://grapr.files.wordpress.com/2011/09/luenberger-linear-and-nonlinear-programming-3e-springer-2008.pdf|David Luenberger - Yinyu Ye: Linear and Nonlinear Programming]].

Here is [[../a4b33opt/literatura|optional literature]].
===== Evaluation of the Course =====

The total number of points is the sum of:

  * Lab homeworks: max. 50 points,
  * Exam: max. 50 points.

Necessary condition for passing the course is passing the labs and min. 25 points from the exam.
The final mark is then determined by the table:

^ points | [0,50) | [50,60) | [60,70) | [70,80) | [80,90) | [90,100] |
^ mark |  F  |  E  |  D  |  C  |  B  |  A  |.


/*
===== Details =====
  * **Teacher:**  Lectures: Boris Flach [[http://cmp.felk.cvut.cz/~flachbor/|web-page]] Labs: Alexander Shekhovtsov [[http://cmp.felk.cvut.cz/~shekhovt/|web-page]]
  * **Course format:** (4/2)
  * ** Schedule:** [[http://www.fel.cvut.cz/cz/education/rozvrhy-ng.B131/public/cz/predmety/12/82/p12820004.html|faculty web-page]]
  * **Lectures:** See here for the [[.materials:lectures|syllabus]]
  * **Labs:** [[.materials:labs|Exercises/Assignments]] will be provided prior to every lab. They will contain theoretical exercises and a practical assignment (homework) Students are expected to work on them and try to solve the theoretical exercises before the lab class. At the class we will discuss solutions as well as possible strategies for the homework assignment. The solution of the latter (report+code) must be submitted by the students within a week after the lab.
  * **Grading policy:** The final mark for the course will be made up of results obtained for the lab homeworks, mid-course tests and the final (written) exam. Details will be announced.
  * **Textbooks and References:**
    * Lecture notes will be available online after each lecture
    * Stephen Boyd and Lieven Vandenberghe: Convex Optimization, Cambridge University Press, 2004 [[http://www.stanford.edu/~boyd/cvxbook/|online]]

*/