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Linear Matrix Inequalities in Control;
In the space of the last two decades or so, Linear Matrix Inequalities (LMIs) have become a de facto standard tool in numerical analysis and design in Control Engineering. Many standard problems, such as stability, robustness, performance and state feedback design, are naturally formulated as LMIs. The advantage of formulating a controller design problem with LMIs is that additional constraints can easily be added. As an example, a linear quadratic regulator (lqr) can be designed subject to constraints on the domain of the closed-loop poles. In addition, various efficient numerical toolboxes have been developed over the years, permitting straightforward usage of LMIs in practical setting.
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Data science using R;
Introducing R as a statistical programming environment for data analysis.
Efficient data management using R.
High-level graphics in R.
Statistical models in R
Aspects of scientific computing in theory and practice.
Reproducible research in practice
Programming in R and vectorized computations.
Optional (if time permits) Matrix factorizations and other numerical methods in R. Easy integration of C++ code in R.
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