Optimization Methods
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This document compares the optimization methods courses between KNITU (Russia) and TUT (Finland). The KNITU course is mandatory, has fewer credits (3 vs 5), and less time spent (108 student hours vs 138). Key topics are similar but KNITU spends less time on lectures (10 vs 28) and nonlinear optimization. The main difference is KNITU has fewer lectures, almost half that of TUT. This could be addressed by using an online math platform like Math-Bridge to provide additional lecture material and practice problems. Mid-term tests on Math-Bridge could help evaluate knowledge gained from the extra online content.
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- 2. ComparisonComparison with standards SEFIwith standards SEFI 2 Unfortunately, I can not find the standards SEFI for the course "Optimization Techniques".
- 3. Comparison with methodologyComparison with methodology of TUTof TUT 3 Optimization Methods (KNITU, RUSSIA) Optimization Methods (TUT, Finland) Selective/mandatory Mandatory Selective method of teaching Blended Blended Course SEFI level (Core 0, 1, 2 or 3) 3 3 Amount of credits 3 5 Duration 12 week 7 weeks Student hours 108 138 Lectures 10 28 Laboratory work / tutorials 20 18 Homework (% mandatory) 35 36 Internship 0 0 Exam preparation 30 10 Exam 3 3 Distribution of study time
- 4. Comparison with methodologyComparison with methodology of TUTof TUT 4 Comparison of topics studied Optimization Methods (KNITU, RUSSIA) Optimization Methods (TUT, Finland) 0. Methods of one-dimensional optimization Passive search method. Blindman-Walking search. Dichotomous search. Golden-section search 1. Linear Optimization 1. Linear Optimization Graphical method , Simplex Algorithm Simplex Algorithm, Duality, Dual Simplex Algorithm 2. Nonlinear Optimization 2. Nonlinear Optimization Free optimization: Minimization by regular simplex. Method of cyclic coordinate descent (CCD), Gradient descent method. The steepest descent method. Newton method. Free optimization (without boundaries), Newton method, Conjugate method, gradient method, quasi-Newton method 3. Constraint Optimization 3. Constraint Optimization Constraint Optimization: Penalty function method. Method of barrier functions. Random search methods. Lagrange method. Constraint Optimization: Karesh-Kuhn- Tucker conditions, penalty and barrier function methods, augmented La Grangean Method
- 5. Comparison with methodologyComparison with methodology of TUTof TUT 5 Using computers Optimization Methods (KNITU, RUSSIA) Optimization Methods (TUT, Finland) Modern lecture technology?multimedia, BlackBoard Matlab Assignments: handed in, tutorials, or both? Tutorials only Tutorials Third party supporting material online? (Such as Khan Academy) None used None used Is there supportive teaching or a support center available? Support available at 20-hour laboratory works Support available at tutorials Which tools are used LMS Black Board, Computer tutorial "Optimization methods", MS Excel, MatLab Moodle, POP, Matlab LMS Black Board: file sharing, course information Moodle: File sharing, course information Computer tutorial: labolatory works. POP: course and exam enrollment, course grades MatLab: labolatory works. Matlab: Tutorials
- 6. Ideas for modifying courseIdeas for modifying course 6 • The main discrepancy with the program TUT: smaller number of lectures (almost twice). Way to solve: • Using Math-Bridge for increase the lecture material • Check the quality of the knowledge gained through mid-term tests..
- 7. Ideas for using Math-Bridge in course.Ideas for using Math-Bridge in course. 7 • Using Math-Bridge for increase the lecture material: To present in Math-Bridge additional topics for study, practical examples of solving problem. • Use Math-Bridge to create and apply mid-term tests on additional topics.
- 8. Thank you for your attention. 8