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Course Information
Course Unit Title : Non-linear Programming
Course Unit Code : 01END5102
Type of Course Unit : Optional
Level of Course Unit : Second Cycle
Year of Study : Preb
Semester : 255.Semester
Number of ECTS Credits Allocated : 6,00
Name of Lecturer(s) :
Course Assistants :
Learning Outcomes of The Course Unit : Linear decision models to recognize and grasp the solution
Linear decision models to analyze and interpret the solutions.
Mode of Delivery : Face-To-Face
Prerequisities and Co-requisities Courses : Unavailable
Recommended Optional Programme Components : Unavailable
Course Contents : Operations Research (OR) 's Definition and Overview, YA'nın Relations with Other Field, the history, YA'nın Applications and Application Examples, Linear Programming (DP) and Properties, DP's Graphics Solution, Various DP Model samples, the DP Model Simplex Solution: Standard Form, Basic Solutions, Simplex Algorithm İlkil, United-M-Method, Two-Phase Method, Dual Simplex Method, Simplex Method in the Special Cases, Binary concept, First-binary Solutions Between the Binary Economic Analysis of Comments -Shadow-Prices Reduced Cost, Sensitivity Analysis, Changes Affecting Eniyiliği, Transport Models, Network Models, Integer Algorithms
Languages of Instruction : Turkish
Course Goals : Operations Research to recognize, as a model of linear optimization problems by solving a variety of ways to gain the ability to interpret.
Course Aims : Students taking this course, to introduce operations research, linear optimization problems solved by various methods to interpret the model's ability to win.
WorkPlacement   Not Available
Recommended or Required Reading
Textbook : Lecture Notes
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 4 56  
Homeworks :
6 3 18  
Presentation :
0 0 0  
Project :
1 10 10  
Lab Study :
0 0 0  
Field Study :
0 0 0  
Visas :
1 20 20  
Finals :
1 30 30  
Workload Hour (30) :
30  
Total Work Charge / Hour :
176  
Course's ECTS Credit :
6      
Assessment Methods and Criteria
Studies During Halfterm :
Number Co-Effient
Visa :
1 50
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 :
50
The ratio of the term to success :
50
The ratio of final to success :
50
TOTAL :
100
Weekly Detailed Course Content
Week Topics  
1 NLP Basics and Features
 
2 Analytical Solution of NLP Decision Model
 
3 Unconstrained Optimization
 
4 Equality Constrained Models
 
5 Lagrange Methods
 
6 Inequality Constrained Models
 
7 Karush?Kuhn?Tucker conditions
 
8 Markov Chains
 
9 Regular Markov Chains
 
10 Absorber Markov Chains
 
11 Inventory Models
 
12 Network Algorithms
 
13 Dijkstra's Algorithms
 
14 Minimum Spanning Tree Algorithms