SF1841 Optimization 6.0 credits
Educational levelFirst cycle
Academic level (A-D)C
Grade scaleA, B, C, D, E, FX, F
PeriodsSpring 13 P3 (6.0 credits)
Start date2013 week: 2
End date2013 week: 11
Language of instructionEnglish
Number of lectures28 (preliminary)
Number of exercises16 (preliminary)
Form of studyNormal
Number of placesNo limitation
ScheduleSchedule (new window)
Course responsiblePer Enqvist <firstname.lastname@example.org>
Master students in Mathematics,
Master students in Aerospace Engineering.
Part of programme
- Master (Two Years), Aerospace Engineering, year 1, SYS, Mandatory
- Master (Two Years), Computer Science, year 1, CSCA, Conditionally Elective
- Master (Two Years), Computer Science, year 1, CSCF, Conditionally Elective
- Master (Two Years), Computer Science, year 1, CSCG, Conditionally Elective
- Master (Two Years), Machine Learning, year 1, MAIA, Conditionally Elective
- Master (Two Years), Machine Learning, year 1, MAIB, Conditionally Elective
- Master (Two Years), Machine Learning, year 1, MAIC, Conditionally Elective
- Master (Two Years), Mathematics, year 1, Mandatory
The overall purpose of the course is that the student should get well acquainted with basic concepts, theory, models and solution methods for optimization. Further, the student should get basic skills in modelling and computer based solving of various applied optimization problems.
Course main content
Examples of applications and modelling training. Basic concepts and theory for optimization, in particular theory for convex problems. Some linear algebra in R^n, in particular bases for the four fundamental subspaces corresponding to a given matrix, and LDLT-factorization of a symmetric definite matrix. Linear optimization, including duality theory. Optimization of flows in networks. Quadratic optimization with linear constraints. Linear least squares problems, in particular minimum norm solutions. Unconstrained nonlinear optimization, in particular nonlinear least squares problems. Optimality conditions for constrained nonlinear optimization, in particular for convex problems. Lagrangian relaxation.
Completed upper secondary education including documented proficiency in English corresponding to English B. And 28 university credits (hp) in mathematics.
More precisely for KTH students:
Passed courses in calculus, linear algebra, differential equations, mathematical statistics, numerical analysis.
The student should have documented knowledge corresponding to university courses in mathematical calculus and analysis, linear algebra, numerical analysis, differential equations and transforms, and mathematical statistics.
Linear and Nonlinear Programming by Nash and Sofer, McGraw-Hill, and some lecture notes.
- TEN1 - Examination, 6.0 credits, grade scale: A, B, C, D, E, FX, F
Requirements for final grade
A written examination (TEN1; 6 hp). Optional homeworks give credit points on the exam.
Per Enqvist <email@example.com>
SF1841 is today identical to SF1811, with common lectures and examination.
Course plan valid from:
Examination information valid from: Spring 08.