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Course module: 191581200
191581200
Continuous Optimization
Course infoSchedule
Course module191581200
Credits (ECTS)6
Course typeCourse
Language of instructionEnglish
Contact personprof.dr. M.J. Uetz
E-mailm.uetz@utwente.nl
Lecturer(s)
Lecturer
Externe Docent
Examiner
prof.dr. M.J. Uetz
Contactperson for the course
prof.dr. M.J. Uetz
Academic year2021
Starting block
1A
RemarksThis course is part of the Mastermath programme.
Application procedureYou apply via OSIRIS Student
Registration using OSIRISYes
Aims
This course aims to provide a concise introduction into the basics of convex and nonconvex continuous constrained optimization.
Content
In continuous optimization the variables take on continuous (as opposed to discrete) values, and the objective and constraints are typically differentiable. This allows for the use of (multivariable) calculus techniques to study the problems and their solutions, and to design and analyze efficient algorithms for finding solutions. In this course we study the theory, algorithms, and applications of continuous optimization. In the theory part we discuss Lagrangian duality, optimality conditions, convexity, and conic programming. In the algorithmic part we discuss first order optimization methods, neural networks/supervised learning, second order optimization methods, and interior point methods, where we also discuss some of the convergence analysis. Throughout we discuss many relevant applications.This course is part of the MasterMath program. Information about the course (description, organization, examination and prerequisites) can be found on http://www.mastermath.nl/. The UT contact person for this course is M.J. Uetz.

Assumed knowledge
The student should have a solid knowledge of linear algebra and multivariable calculus. The student should also have knowledge of linear programming (including linear programming duality) and convex analysis to the level of being able to follow the text and do the exercises from:
  • Chapters 1 and 2 including all exercises from the book 'Linear Programming, A Concise Introduction, Thomas S. Ferguson' (https://www.math.ucla.edu/~tom/LP.pdf)
  • Exercises 2.1, 2.2, 2.12, 3.1, 3.3, 3.5, and 3.7 from the book 'Convex Optimization, Stephen Boyd and Lieven Vandenberghe' (http://stanford.edu/~boyd/cvxbook)
Participating study
Master Applied Mathematics
Required materials
-
Recommended materials
Course material
The materials will be available online or provided by the lecturer.
Instructional modes
Lecture

Tests
Written exam

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Kies de Nederlandse taal