In this paper we extend the smoothing technique [7], [9] onto the problems of Semidefinite Optimization. For that, we develop a simple framework for estimating a Lipschitz constant for the gradient of some symmetric functions of eigenvalues of symmetric matrices. Using this technique, we can...

Most considerations of knowledge management focus on corporations and, until recently, considered knowledge to be objective, stable, and asocial. In this paper we wish to move the focus away from corporations, and examine knowledge and national innovation systems. We argue that the knowledge...

Most considerations of knowledge management focus on corporations and, until recently, considered knowledge to be objective, stable, and asocial. In this paper we wish to move the focus away from corporations, and examine knowledge and national innovation systems. We argue that the knowledge...

In this paper we introduce a new primal-dual technique for convergence analysis of gradient schemes for non-smooth convex optimization. As an example of its application, we derive a primal-dual gradient method for a special class of structured non-smooth optimization problems, which ensures a...

In this paper we suggest new dual methods for solving variational inequalities with monotone operators. We show that with an appropriate step-size strategy, our method is optimal both for Lipschitz continuous operators (O(1/e)iterations), and for the operators with bounded variations(0 (1/e2))....

In this paper we propose a new approach for constructing efficient schemes for nonsmooth convex optimization. It is based on a special smoothing technique, which can be applied to the functions with explicit max-structure. Our approach can be considered as an alternative to black-box...