The requested page is not available in the preferred language.
The requested article is available in the following languages.
Kursens nivå: Forskarnivå
Antal poäng: 9 hp
Examinator: Yurii Malitskyi
- A. Beck. First-Order Methods in Optimization, 2017.
- D. Bertsekas. Convex optimization algorithms, 2015.
- S. Boyd, L. Vandenberghe. Convex optimization, 2015.
Kursinnehåll och lärandemål: First-order methods (unconstrained and constrained optimization, accelerated methods, stochastic methods, nonsmooth methods, non-Euclidean methods), saddle point problems, variational inequalities, second-order methods. The goal of the course is to give a broad overview of various optimization algorithms. Students will be introduced to popular modern optimization techniques in nonlinear optimization and better understand the need for optimization in machine learning, engineering, and data science.
Organisation inkl. obligatoriska moment: One lecture per week
Examination: One scribed lecture, homework assignments.
Förkunskaper: Calculus, linear algebra
Betygsskala: Pass / Failed
Last updated: 2022-03-03