Eskisehir Technical University Info Package Eskisehir Technical University Info Package
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About the Program Educational Objectives Key Learning Outcomes Course Structure Diagram with Credits Field Qualifications Matrix of Course& Program Qualifications Matrix of Program Outcomes&Field Qualifications
  • Faculty of Engineering
  • Department of Industrial Engineering
  • Course Structure Diagram with Credits
  • Theory and Algorithms in Nonlinear Programming
  • Description
  • Description
  • Learning Outcomes
  • Course's Contribution to Prog.
  • Learning Outcomes & Program Qualifications

Course Introduction Information

Code - Course Title ENM325 - Theory and Algorithms in Nonlinear Programming
Course Type Area Elective Courses
Language of Instruction İngilizce
Laboratory + Practice 2+1
ECTS 5.0
Course Instructor(s) DOKTOR ÖĞRETİM ÜYESİ GÜLÇİN DİNÇ YALÇIN
Mode of Delivery Face to face
Prerequisites Calculus 1, Calculus 2, Linear Algebra, Linear Programming
Courses Recomended -
Recommended Reading List • Wayne L. Winston, “Operations Research, Applications and Algorithms”, 4th Edition, Duxbury Pres Thomson Learning, Inc., 2004.• İmdat Kara, “Yöneylem Araştırması Doğrusal Olmayan Modeller”, Anadolu Üniversitesi Yayınları No.139, Eskişehir 1986.• Hamdy A. Taha, “Operations Research: An Introduction”, 8th Edition, Prentice Hall, Upper Saddle River, N.J., 2007. LP Softwares (Gams, Lingo)
Assessment methods and criteria 2 Midterms, Assignments, Final
Work Placement -
Sustainability Development Goals

Content

Weeks Topics
Week - 1 Introduction to Nonlinear Programming (NLP)
Week - 2 Convex sets
Week - 3 Convex and Concave Functions
Week - 4 One Variable Unconstraints Optimization Problems
Week - 5 Bisection Method to Solve One Variable Unconstraint NLP Models
Week - 6 Bisection Method to Solve One Variable Unconstraint NLP Models
Week - 7 Multi Variables Unconstraint Optimization Problems
Week - 8 Gradient Search Method to Solve Muşti Variable Unconstraint NLP Models
Week - 9 General Form of NLP Medol, Karush-Kuhn Tucker (KKT) Conditions
Week - 10 KKT Conditions for Equality Constraints and Inequality Constraint Problems
Week - 11 Introduction to Convex Programming
Week - 12 Introduction to Convex Programming
Week - 13 Frank-Wolfe Algorithm
Week - 14 Sequential Unconstrained Minimization Technique (SUMT)

Learning Activities and Teaching Methods

  • Teaching Methods
  • Lecture
  • Question & Answer
  • Team/Group Work
  • Drill - Practise
  • Case Study
  • Report Preparation and/or Presentation

Assessment Methods

Assessment Method and Passing Requirements
Quamtity Percentage (%)
1.Midterm Exam 1 30
Homework 1 30
Final Exam 1 40
Toplam (%) 100
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