Consensus over ring networks as a quadratic optimal control problem

This paper presents the consensus problem in the framework of optimal control. Our aim is to synchronize a set of identical linear systems. We propose a cost which penalizes mutual differences between the states of these systems. The feedback matrix resulting from this linear quadratic control problem represents the interconnection network which synchronizes the systems. In general the interconnection structure is of the all-to-all type. We show that it is possible to devise an LQR problem in which the cost results in an interconnection structure representing ring coupling. Care has to be taken that the effect of the feedback control is restricted to synchronizing the systems, i.e. when the systems are synchronized, the feedback control signal is required to be equal to zero

Modified Frank-Wolfe Algorithm for Enhanced Sparsity in Support Vector Machine Classifiers

This work proposes a new algorithm for training a re-weighted L2 Support Vector Machine (SVM), inspired on the re-weighted Lasso algorithm of Cand\`es et al. and on the equivalence between Lasso and SVM shown recently by Jaggi. In particular, the margin required for each training vector is set independently, defining a new weighted SVM model. These weights are selected to be binary, and they are automatically adapted during the training of the model, resulting in a variation of the Frank-Wolfe optimization algorithm with essentially the same computational complexity as the original algorithm. As shown experimentally, this algorithm is computationally cheaper to apply since it requires less iterations to converge, and it produces models with a sparser representation in terms of support vectors and which are more stable with respect to the selection of the regularization hyper-parameter