A Flexible Framework for Cubic Regularization Algorithms for Nonconvex Optimization in Function Space. (2nd January 2019) - Schiela, Anton
Abstract: We propose a cubic regularization algorithm that is constructed to deal with nonconvex minimization problems in function space. It allows for a flexible choice of the regularization term and thus accounts for the fact that in such problems one often has to deal with more than one norm. Global and local convergence results are established in a general framework.