TITLE: Efficiency of random search methods on huge-scale optimization problems

SPEAKER:  Yurii Nesterov

ABSTRACT:

In this talk we describe the new methods for solving
huge-scale optimization problems. For problems of this
size, even the simplest full-dimensional vector operations
are very expensive. Hence, we suggest to apply an
optimization technique based on random partial update of

decision variables. For these methods, we prove the global
estimates for the rate of convergence. Surprisingly
enough, for certain classes of objective functions, our
results are better than the standard worst-case bounds for
deterministic algorithms. We present constrained and
unconstrained versions of the method, and its accelerated
variant. Our numerical test confirms a high efficiency of
this technique on problems of very big size.