[ 536MATHNUOK19 ] KV Numerical Optimization

Workload Education level Study areas Responsible person Hours per week Coordinating university
4,5 ECTS B2 - Bachelor's programme 2. year Mathematics Sepp Hochreiter 3 hpw Johannes Kepler University Linz
Detailed information
Original study plan Bachelor's programme Artificial Intelligence 2019W
Objectives This course provides an introduction to the theory and to algorithms in linear, non-linear, constrained, and unconstrained numerical optimization.
  • Introduction
  • Fundamentals of Unconstrained Optimization
  • Line Search Methods
  • Trust-Region Methods
  • Conjugate Gradient Methods
  • Practical Newton Methods
  • Quasi-Newton Methods
  • Levenberg-Marquardt Algorithm
  • Predictor Corrector Methods
  • Large-Scale Quasi-Newton and Partially Separable Optimization
  • Least-Squares Problems
  • Fundamentals of Constrained Optimization
  • Linear Programming: Simplex and Interior-Point Methods
  • Quadratic Programming
  • Penalty, Barrier, and Augmented Lagrangian Methods
  • Sequential Quadratic Programming
  • Convex Optimization
  • Online Optimization and Stochastic Algorithms
Criteria for evaluation
Language English
Changing subject? No
On-site course
Maximum number of participants -
Assignment procedure Direct assignment