In this paper, we analyze the regret incurred by a computationally efficient exploration
strategy, known as naive exploration, for controlling unknown partially observable systems …

In this contribution we consider sparse linear regression problems. It is well known that the
mutual coherence, ie the maximum correlation of the regressors, is important for the ability of …

In optimal input design input sequences are typically generated without paying attention to
the correlations between the regressors of the model to be estimated. In fact, in many cases …

The problem of solving under-determined systems of linear equations with minimum peak
magnitude (ℓ∞ norm) has numerous applications in signal processing. These include Peak …

Having an accurate model of a system is essential for many applications today, especially
those related to advanced process control (APC). When executing an industrial delivery …

While designing excitation signals for identification of industrial processes, it is important to
obtain desired model accuracies, reduce the experimental time and limit the output …

Many off-the-shelf generic non-linear model structures have inherent sparse
parametrizations. Volterra series and non-linear Auto-Regressive with eXogeneous inputs …

Sparse parameter estimation is an important aspect of system identification, as it allows for
reducing the order of a model, and also some models in system identification inherently …