We observe that successive applications of known results from the theory of positive systems lead to an efficient general algorithm for positive realizations of transfer functions...
We propose a general and efficient algorithm for learning low-rank matrices. The proposed algorithm converges super-linearly and can keep the matrix to be learned in a compact fac...
Most of the work which attempts to give bounds on the generalization error of the hypothesis generated by a learning algorithm is based on methods from the theory of uniform conve...
This paper presents the virtual gene genetic algorithm (vgGA) which is a generalization of traditional genetic algorithms that use binary linear chromosomes. In the vgGA, tradition...
In this paper we introduce a new general framework for set covering problems, based on the combination of randomized rounding of the (near-)optimal solution of the Linear Programm...