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Course Information
Course Unit Title : Optimization Technique
Course Unit Code : 01MAK5143
Type of Course Unit : Optional
Level of Course Unit : Second Cycle
Year of Study : 1
Semester : 1.Semester
Number of ECTS Credits Allocated : 6,00
Name of Lecturer(s) : ---
Course Assistants :
Learning Outcomes of The Course Unit : An ability to find and use information required to design a system, component or process
A knowledge of and ability to apply theoretical and applied mathematics to model, analyze and solve engineering problems
Mode of Delivery : Face-To-Face
Prerequisities and Co-requisities Courses : Unavailable
Recommended Optional Programme Components : Unavailable
Course Contents : Introduction, Unconstrained optimization, Objective function and variables, Constrained optimization
( Lagrangians and Augmented Lagrangian Methods, Linear and Quadratic Programming , Nonlinear programming ,Duality ) , Review of various methods , Genetic Algorithm , Approach to the game
Languages of Instruction : Turkish
Course Goals : 1.Solve a multi-objective problem through weighted and constrained methods
2.Acquire an idea about the various direct and indirect search methods
3.Understand evolutionary algorithms
Course Aims : The establishment of mathematical models provides the optimal decision-making and encountered in real life, showing areas of application and implementation of solution methods.An ability to find and use information required to design a system, component or process
WorkPlacement   Not Available
Recommended or Required Reading
Textbook : 1.Rao S. S., 1991, Optimization: Theory and application, 2nd edition, Willey.
Additional Resources :
Material Sharing
Documents :
Assignments :
Exams :
Additional Material :
Planned Learning Activities and Teaching Methods
Lectures, Practical Courses, Presentation, Seminar, Project, Laboratory Applications (if necessary)
ECTS / Table Of Workload (Number of ECTS credits allocated)
Student workload surveys utilized to determine ECTS credits.
Activity :
Number Duration Total  
Course Duration (Excluding Exam Week) :
14 3 42  
Time Of Studying Out Of Class :
14 9 126  
Homeworks :
0 0 0  
Presentation :
0 0 0  
Project :
0 0 0  
Lab Study :
0 0 0  
Field Study :
0 0 0  
Visas :
1 6 6  
Finals :
1 8 8  
Workload Hour (30) :
30  
Total Work Charge / Hour :
182  
Course's ECTS Credit :
6      
Assessment Methods and Criteria
Studies During Halfterm :
Number Co-Effient
Visa :
1 100
Quiz :
0 0
Homework :
0 0
Attendance :
0 0
Application :
0 0
Lab :
0 0
Project :
0 0
Workshop :
0 0
Seminary :
0 0
Field study :
0 0
   
TOTAL :
100
The ratio of the term to success :
40
The ratio of final to success :
60
TOTAL :
100
Weekly Detailed Course Content
Week Topics  
1 Introduction
  Study Materials: Researching of related to topics
2 Unconstrained optimization
  Study Materials: Researching of related to topics
3 Unconstrained optimization
  Study Materials: Researching of related to topics
4 Objective function and variables
  Study Materials: Researching of related to topics
5 Constrained optimization
Lagrangians and Augmented Lagrangian Methods
  Study Materials: Researching of related to topics
6 Constrained optimization
Linear and Quadratic Programming
  Study Materials: Researching of related to topics
7 Mid - term Exam
 
8 Constrained optimization
Nonlinear programming
  Study Materials: Researching of related to topics
9 Constrained optimization
Duality
  Study Materials: Researching of related to topics
10 Review of various methods
  Study Materials: Researching of related to topics
11 Review of various methods
Genetic Algorithm
  Study Materials: Researching of related to topics
12 Review of various methods
Genetic Algorithm
  Study Materials: Researching of related to topics
13 Review of various methods
Approach to the game
  Study Materials: Researching of related to topics
14 Final Exam