The Alternating Descent Conditional Gradient Method for Sparse Inverse Problems
The Alternating Descent Conditional Gradient Method for Sparse Inverse Problems
We propose a variant of the classical conditional gradient method for sparse inverse problems with differentiable observation models. Such models arise in many practical problems including superresolution microscopy, time-series modeling, and matrix completion. Our algorithm combines nonconvex and convex optimization techniques: we propose global conditional gradient steps alternating with nonconvex …