Optimization techniques Field of study: Computer Science
Programme code: 08-S2INIA15.2019

Module name: Optimization techniques
Module code: 08-IN-IJO-S2-TO
Programme code: 08-S2INIA15.2019
Semester: winter semester 2020/2021
Language of instruction: English
Form of verification: exam
ECTS credits: 3
Description:
Aim of the classes in this module is preparing the students to solve the tasks of discreet optimization. As a result, the student should exhibit full understanding of issues connected with designing and implementation of classic and modern optimization algorithms. In consequence it should lead to deepening the knowledge of efficient algorithms designing and developing the skill of their implementation.
Prerequisites:
(no information given)
Key reading:
(no information given)
Learning outcome of the module Codes of the learning outcomes of the programme to which the learning outcome of the module is related [level of competence: scale 1-5]
Can co-operate with a partner executing his share of task. [TO -K_9]
 K_U02 [1/5]
Can properly use chosen programming libraries to formulate discreet optimization problems as a task of linear programming (including integer). [TO -U_5]
 K_U01 [1/5] K_U14 [1/5]
Can solve a task of linear programming (including integer)using available libraries in the chosen programming language. [TO -U_6]
 K_U07 [1/5] K_U12 [1/5]
Is able to design and implement distribution and restrictions algorithm and the method of dynamic programming for the given discreet optimization problem. [TO -U_7]
 K_U12 [1/5] K_U13 [1/5] K_U14 [1/5]
Can implement chosen meta-heuristics. [TO -U_8]
 K_U12 [1/5] K_U14 [1/5] K_U16 [1/5]
Has knowledge in the field of formulating discreet optimization tasks using linear, integer and zero-one programming. [TO -W_1]
 K_W09 [2/5]
Possesses knowledge in the field of classic optimization techniques such as method of distribution and restrictions and dynamic programming and can characterize expedience of their use. [TO -W_2]
 K_W09 [2/5]
Has knowledge in the field of chosen meta-heuristics: local browsing, annealing and taboo search. [TO -W_3]
 K_W09 [1/5]
Has knowledge about class of optimization problems and, especially in this aspect can recognize that it is impossible to design polynomial algorithm for a given task. [TO -W_4]
 K_W01 [1/5] K_W02 [1/5] K_W03 [1/5]
Type Description Codes of the learning outcomes of the module to which assessment is related
Exam [TO -w_1]
Solving tasks of content, one after each section discussed during lectures.
 TO -W_1 TO -W_2 TO -W_3 TO -W_4
Laboratory class credit [TO -w_2]
Tests after each topic completed during classes together with lecture theoretical knowledge control.
 TO -K_9 TO -U_5 TO -U_6 TO -U_7 TO -U_8
Form of teaching Student's own work Assessment of the learning outcomes
Type Description (including teaching methods) Number of hours Description Number of hours
lecture [TO -fs_1]
Presenting educational content in verbal form, with use of content visualization. Focusing on conceptually complex material and indicating website addresses.
 15
Familiarizing with lecture content using the existing packages of methods and websites.
 15 Exam [TO -w_1]
laboratory classes [TO -fs_2]
Detailed preparation of the students to solve tasks indicating proceedings methodology and sequence of proceeding. Solving tasks of content.
 30
Solving tasks (mainly referring to implementation) of subsequent topics together with analysis of the solutions already existing on websites.
 30 Laboratory class credit [TO -w_2]
Attachments
Module description (PDF)
Information concerning module syllabuses might be changed during studies.
Syllabuses (USOSweb)
Semester Module Language of instruction
(no information given)