Before choosing course
This course has been discontinued.
Last planned examination: Spring 2016
Decision to discontinue this course: No information inserted
Course offering missing
Course offering missing for current semester as well as for previous and coming semestersContent and learning outcomes
Course contents
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.
Intended learning outcomes
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 Disposition
No information inserted
Literature and preparations
Specific prerequisites
In general:
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.
Recommended prerequisites
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.
Equipment
No information inserted
Literature
Linear and Nonlinear Programming by Nash and Sofer, McGraw-Hill, and some lecture notes.
Examination and completion
If the course is discontinued, students may request to be examined during the following two academic years.
Grading scale
A, B, C, D, E, FX, F
Examination
- TEN1 - Examination, 6,0 hp, betygsskala: A, B, C, D, E, FX, F
Based on recommendation from KTH’s coordinator for disabilities, the examiner will decide how to adapt an examination for students with documented disability.
The examiner may apply another examination format when re-examining individual students.
Other requirements for final grade
A written examination (TEN1; 6 hp). Optional homeworks give credit points on the exam.
Opportunity to complete the requirements via supplementary examination
No information inserted
Opportunity to raise an approved grade via renewed examination
No information inserted
Examiner
Ethical approach
- All members of a group are responsible for the group's work.
- In any assessment, every student shall honestly disclose any help received and sources used.
- In an oral assessment, every student shall be able to present and answer questions about the entire assignment and solution.
Further information
Course web
Further information about the course can be found on the Course web at the link below. Information on the Course web will later be moved to this site.
Course web SF1841Offered by
Main field of study
Mathematics, Technology
Education cycle
First cycle
Add-on studies
SF2812, SF2822.
Supplementary information
SF1841 is today identical to SF1811, with common lectures and examination.