Lipschitz continuity of the gradient mapping of a continuously differentiable function plays a crucial role in designing various optimization algorithms. However, many functions arising in practical applications such as low rank matrix factorization or deep neural network problems do not have a Lipschitz continuous gradient. This led to the development of a generalized notion known as the L-smad property, which is based on generalized proximity measures called Bregman distances. We propose the MAP property, which generalizes the L-smad property and is also valid for a large class of nonconvex nonsmooth composite problems. Based on the proposed MAP property, we develop a globally convergent algorithm called Model BPG and an inertial variant, that unifies several existing algorithms.
An Inertial Non-smooth Non-convex Bregman Minimization Framework
22.11.2021 15:30 - 16:30
Organiser:
R. I. Boț (U Wien), S. Sabach (Technion - Israel Institute of Technology Haifa), M. Staudigl (Maastricht U)
Location:
Zoom Meeting
Verwandte Dateien
- abstract_Ochs_OWOS.pdf 149 KB