**Background:**
Many students in Computer Science do not have a sufficient
background in applied mathematics to employ state-of-the-art
optimization techniques and to judge the outcome of such
techniques critically (e.g. regarding the
stability/quality/accuracy of their output). At the same time,
the use of optimization techniques in computer graphics is
becoming ubiquitous. Treating optimization algorithms as a
black box yields sub-optimal results at best. At worst,
stability issues and convergence problems may prevent the
solution of a problem or impede the general application of a
method to a wide range of input, i.e. beyond the set of
examples shown in a paper. The course will draw attention to
these aspects and to current best practices. This will enable
participants to judge articles that use optimization schemes
critically and improve their own skill set.

**Scope and Intended Audience:**
We aim at thoroughly covering the basic techniques in
optimization, only requiring a good working knowledge of the
mathematical foundations in a standard CS curriculum, in
particular, multi-dimensional analysis and linear
algebra. Part of the course will be suitable for a starting
PhD student. On the other end, our goal is to lead up to
current research including modern ideas such as compressed
sensing, convex variational formulations, and
sparsity-inducing norms. We aim at exposing the major
underlying ideas, exposing the working principles and giving
hints for a successful implementation. The course thus also
caters to the experienced researcher that seeks to utilize
these modern techniques. We approach these goals by discussing
a mixture of classic and more modern optimization
approaches. Each section is presented by an expert in the
area. Further, each section is comprised of two major parts:
1.) a condensed introduction of the necessary background and
2.) its application in particular graphics problems. We aim at
giving implementation hints and the exposure of
current-best-practices.

Linear and Non-linear Least Squares Fitting (Granier) | [pptx] |

Numerical Linear Algebra (Guennebaud) | [odp] |

Inverse Problems (Ihrke) | [pptx] |

Variational Methods (Goldlücke) | [pdf] |

Compressive Sensing (Jacques) | [pdf] |

If you use the materials above in your own presentations, please
acknowledge the authors. If you find mistakes, or if you want to
share updated materials, don't hesitate to contact us.