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Course Description Course Name : Optimization Techniques - II Course Code : İSB222 Course Type : Compulsory Level of Course : First Cycle Year of Study : 2 Course Semester : Spring (16 Weeks) ECTS : 5 Name of Lecturer(s) : Prof.Dr. SELAHATTİN KAÇIRANLAR Learning Outcomes of the Course : understand unconstrained optimization the problems and solutions use unrestricted multivariate methods for the solution of optimization problems understand the multi-dimensional optimization problems with equality constraints apply Jacobian and the methods of Lagrange Understand inequality constrained optimization problems Write and solve the Kuhn-Tucker Conditions understand search methods, to distinguish the relationship between them apply search methods for solving optimization problems Mode of Delivery : Face-to-Face Prerequisites and Co-Prerequisites : None Recommended Optional Programme Components : None Aim(s) of Course : to solve unconstrained and constrained optimization problems, to learn search methods Course Contents : Unrestricted problems, equality restricted optimization problems, inequality restricted optimization problems, nonlinear programming, single and multi variable unrestricted optimization methods, restricted optimization methods, geometric programming, target programming. Language of Instruction : Turkish Work Place : Faculty of Arts and Sciences Annex Classrooms   Course Outline /Schedule (Weekly) Planned Learning Activities Week Subject Student's Preliminary Work Learning Activities and Teaching Methods 1 Unconstrained Optimization Source reading Lecture discussion and problem-solving 2 Unrestricted Multivariable Optimization Source reading Lecture discussion and problem-solving 3 Unrestricted Multivariable Optimization Source reading Lecture discussion and problem-solving 4 Multidimensional Equality Constrained Optimization Problems Source reading Lecture discussion and problem-solving 5 Multidimensional Equality Constrained Optimization Problems Source reading Lecture discussion and problem-solving 6 Inequality Constrained Optimization Problems Source reading Lecture discussion and problem-solving 7 Kuhn-Tucker conditions Source reading Lecture discussion and problem-solving 8 Mid-term Exam Rewview the topics discussed in the lecture notes and sources Written exam 9 Univariate Search Techniques Source reading Lecture discussion and problem-solving 10 Full Search, Search Two Points of Symmetric Source reading Lecture discussion and problem-solving 11 Fibonacci Searchı Source reading Lecture discussion and problem-solving 12 Search Two Points of Symmetric Source reading Lecture discussion and problem-solving 13 Golden Ratio Search Source reading Lecture discussion and problem-solving 14 Split into three Search Source reading Lecture discussion and problem-solving 15 Problem Solving Problem Solving problem-solving 16/17 Final exam Rewview the topics discussed in the lecture notes and sources Written exam   Required Course Resources Resource Type Resource Name Recommended Course Material(s)  Yöneylem Araştırması, Hamdy A. Taha(Çevirenler : Ş. Alp Baray- Şakir Esnaf), Literatür Yayıncılık, 2000 Required Course Material(s)  Yöneylem Araştırması, Ahmet Öztürk, Ekin Yayınevi,2009  Optimizasyon, Ayşen Apaydın,A.Ü.F.F. Dön. Ser. Yayınları, 1996  Rangarajan K. Sundaram (1996). A First Course in Optimization Theory, Cambridge University Pres.  Optimizasyon Teknikleri, Hasan Bal,Gazi Üniversitesi Yayınları, 1995   Assessment Methods and Assessment Criteria Semester/Year Assessments Number Contribution Percentage     Mid-term Exams (Written, Oral, etc.) 1 80     Homeworks/Projects/Others 5 20 Total 100 Rate of Semester/Year Assessments to Success 40   Final Assessments 100 Rate of Final Assessments to Success 60 Total 100   Contribution of the Course to Key Learning Outcomes # Key Learning Outcome Contribution* 1 Utilize computer systems and softwares 2 2 Apply the statistical analyze methods 0 3 Make statistical inference(estimation, hypothesis tests etc.) 0 4 Generate solutions for the problems in other disciplines by using statistical techniques 0 5 Discover the visual, database and web programming techniques and posses the ability of writing programme 0 6 Construct a model and analyze it by using statistical packages 2 7 Distinguish the difference between the statistical methods 0 8 Be aware of the interaction between the disciplines related to statistics 4 9 Make oral and visual presentation for the results of statistical methods 1 10 Have capability on effective and productive work in a group and individually 2 11 Develop scientific and ethical values in the fields of statistics-and scientific data collection 1 12 Explain the essence fundamentals and concepts in the field of Probability, Statistics and Mathematics 4 13 Emphasize the importance of Statistics in life 1 14 Define basic principles and concepts in the field of Law and Economics 0 15 Produce numeric and statistical solutions in order to overcome the problems 4 16 Construct the model, solve and interpret the results by using mathematical and statistical tehniques for the problems that include random events 5 17 Use proper methods and techniques to gather and/or to arrange the data 0 18 Professional development in accordance with their interests and abilities, as well as the scientific, cultural, artistic and social fields, constantly improve themselves by identifying training needs 0 * Contribution levels are between 0 (not) and 5 (maximum).   Student Workload - ECTS Works Number Time (Hour) Total Workload (Hour) Course Related Works     Class Time (Exam weeks are excluded) 14 3 42     Out of Class Study (Preliminary Work, Practice) 14 3 42 Assesment Related Works     Homeworks, Projects, Others 5 3 15     Mid-term Exams (Written, Oral, etc.) 1 10 10     Final Exam 1 15 15 Total Workload: 124 Total Workload / 25 (h): 4.96 ECTS Credit: 5