Studienhandbuch | KV Numerical Optimization

Inhalt

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.
Subject	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