Abstract. We address the problem of continuous stochastic optimal control in the presence of hard obstacles. Due to the non-smooth character of the obstacles, the traditional appro...
Stochastic gradient descent (SGD) uses approximate gradients estimated from subsets of the training data and updates the parameters in an online fashion. This learning framework i...
: Based on previous work of the authors, this paper provides a comparison of two different tracking methodologies for extended objects and group targets, where the true shape of th...
Marcus Baum, Michael Feldmann, Dietrich Fraenken, ...
We study the randomized approximation of weakly singular integral operators. For a suitable class of kernels having a standard type of singularity and being otherwise of finite sm...
Stochastic perturbation methods can be applied to problems for which either the objective function is represented analytically, or the objective function is the result of a simula...