Credits: 5

Schedule: 10.09.2019 - 05.12.2019

Teaching Period (valid 01.08.2018-31.07.2020): 

I-II Autumn (2018-2019, 2019-2020)

Learning Outcomes (valid 01.08.2018-31.07.2020): 

The course presents concepts and properties related to convexity. The students learn to interpret and explain different optimality conditions and use them to calculate optimal solutions. The students also learn to analyze different optimization algorithms and use them to solve optimization problems.

Content (valid 01.08.2018-31.07.2020): 

The first part of the course teaches the optimization theory: convexity, necessary and sufficient optimality condition and their derivation, the interpretation of Lagrange multipliers, and duality. The second part teaches numerical optimization: unconstrained, convex, and constrained optimization. Applications from natural sciences, engineering and economics.

Assessment Methods and Criteria (valid 01.08.2018-31.07.2020): 

Exam and home assignments.

Workload (valid 01.08.2018-31.07.2020): 

Contact hours 48 h. Attendance is not compulsory.

Home exercises 15h 

Autonomous studies 70h

Study Material (valid 01.08.2018-31.07.2020): 

Lecture notes available at course's homepage and bibliography to be announced at the beginning of the course.

Substitutes for Courses (valid 01.08.2018-31.07.2020): 

Mat-2.3139 Nonlinear Programming P, MS-E2139 Nonlinear Programming

Course Homepage (valid 01.08.2018-31.07.2020):

Prerequisites (valid 01.08.2018-31.07.2020): 

MS-A00XX Matrix Algebra, MS-A01XX Differential and integral calculus 1, and MS-A02XX Differential and integral calculus 2.

Grading Scale (valid 01.08.2018-31.07.2020): 



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