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Nonlinear optimization

Kursens nivå: Forskarnivå

Antal poäng: 9 hp

Examinator: Yurii Malitskyi


  1. A. Beck. First-Order Methods in Optimization, 2017.
  2. D. Bertsekas. Convex optimization algorithms, 2015.
  3. 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

Undervisningsspråk: English

Kursens hemsida

Page manager: karin.johansson@liu.se
Last updated: 2022-03-03