###
FSF3810 Convexity and optimization in linear spaces, 7,5 hp,
Spring 2023.

This course deals with
optimization theory in infinite
dimensional vector spaces.
It is one of the core courses in the doctoral program Applied and
Computational Mathematics at KTH.
###
Lecturer and examiner:

Johan Karlsson,
email: johan.karlsson _at_ math.kth.se,
room 3550, Lindstedtsv 25, tfn 790 8440
###
Main content:

Basic theory for normed linear spaces.

Minimum norm problems in Hilbert and Banach spaces.

Convex sets and separating hyperplanes.

Adjoints and pseudoinverse operators.

Convex functionals and their corresponding conjugate functionals.

Fenchel duality.

Global theory of constrained convex optimization.

Lagrange multipliers and dual problems.

Gateaux and Frechet differentials.

Local theory of constrained optimization.

Kuhn-Tucker optimality conditions in Banach spaces.

###
Prerequisites:

Mathematics corresponding approximately to a
Master of science in engineering physics,
including a basic course in optimization.
###
Literature:

David G Luenberger: Optimization by vector space methods,

John Wiley & Sons. Paperback, ISBN: 0-471-18117-X.
###
Reading instructions and preliminary plan for the lectures.

Preliminary course plan.
###
Examination:

Mandatory homework assignments and
a mandatory oral exam.

There will be in total six collections of
homeworks, one collection every second week.