  Modern 3D image recovery problems require powerful optimization frameworks to
handle high dimensionality while providing reliable numerical solutions in a
reasonable time. In this perspective, asynchronous parallel optimization
algorithms have received an increasing attention by overcoming memory
limitation issues and communication bottlenecks. In this work, we propose a
block distributed Majorize-Minorize Memory Gradient (BD3MG) optimization
algorithm for solving large scale non-convex differentiable optimization
problems. Assuming a distributed memory environment, the algorithm casts the
efficient 3MG scheme into smaller dimension subproblems where blocks of
variables are addressed in an asynchronous manner. Convergence of the sequence
built by the proposed BD3MG method is established under mild assumptions.
Application to the restoration of 3D images degraded by a depth-variant blur
shows that our method yields significant computational time reduction compared
to several synchronous and asynchronous competitors, while exhibiting great
scalability potential.

Optimization and Control;Distributed, Parallel, and Cluster Computing