Optimization Theory

Mode of Delivery

Face to Face

Prerequisites and Co-Requisites

None

Recommended Optional Programme Components

Not Required

Course Contents

This course equips candidates with in-depth knowledge on the classification of analitical optimization and techniques, unlimited,linear limited ,nonlinear limited optimization, lagrange multiplier method, Kuhn-Tucker rules,punishment functions,linear,squre and unlinear programing, engineering applications, dynamic optimization and Heuristic optimization

Weekly Schedule

1) Practice of course, reference books, content of course, introduction
2) Mathematical Fundamentals, Some basic vector functions, Differential computing
3) Classification and techniques of analytical optimization, Basic absolute optmization, One-dimensional searching methods-Golden Search, Fibonacci search, Newton method
4) Gradyan method, Newton method, Konjuge method
5) Solution of linear equation system, smallest squares anlysis, analysis of linear equation system, recursive smallest squares algorithm, minimization of linear equation system (Ax=b, min )
6) Artificial neurol networks and absolute optimization, one-neuron neurol network, Back-diffusion and gradyan algorithm
7) Artificial neurol networks and absolute optimization, one-neuron neurol network, Back-diffusion and gradyan algorithm
8) Midterm examination
9) Linear, Kuadratik and Non-linear programming, Linear programming, Simplex method, Kuadratik programming, non-linear programming and reduction
10) Non-linear Optimization, Equality-restricted problems, Required conditions Secondary conditions
11) Non-linear Optimization, Equality-restricted problems, Required conditions Secondary conditions
12) Limited optimization with Lagrange Multiplier method, Non-linear restrictions, Applications
13) Unequality Restrictions 4.8 Kuhn-Tucker Conditions
14) Konveks optmization problems, Konveks functions, Dynamic Systems and Dynamic optimization, Dynamic systems, Dynamic restricted optimization, applications, introduction to Heuristic Optimization
15) Project Presentations
16) Final examination

Recommended or Required Reading

Planned Learning Activities and Teaching Methods

1) Lecture
2) Discussion
3) Demonstration
4) Group Study
5) Self Study
6) Problem Solving

Assessment Methods and Criteria

Contribution of Semester Studies to Course Grade	50%
Number Percentage Semester Studies Midterm Examination 1 40% Project 1 60%
Contribution of Final Examination to Course Grade	50%
Total	100%

Language of Instruction

Turkish

Work Placement(s)

Not Required