Stochastic optimization problems provide a means to model uncertainty in the input data where the uncertainty is modeled by a probability distribution over the possible realizatio...
— We present a new optimization procedure which is particularly suited for the solution of second-order kernel methods like e.g. Kernel-PCA. Common to these methods is that there...
In this paper we discuss the design of optimization algorithms for cognitive wireless networks (CWNs). Maximizing the perceived network performance towards applications by selectin...
We propose "low cost response surface methods" (LCRSM) that typically require half the experimental runs of standard response surface methods based on central composite ...
The focus is on black-box optimization of a function f : RN R given as a black box, i. e. an oracle for f-evaluations. This is commonly called direct search, and in fact, most meth...