This website accompanies the paper:

- MOCCA: mirrored convex/concave optimization for nonconvex composite functions.

Rina Foygel Barber and Emil Y. Sidky. To appear in Journal of Machine Learning Research. arXiv:1510.08842

Our paper proposes an algorithm for minimizing a nonconvex composite function of the form

*F(Kx)+G(x)*, where

*F*and

*G*may be nonconvex and/or nondifferentiable and

*K*is a known linear transformation. The method is a primal/dual algorithm where, at each iteration, we take a local convex approximation to

*F*and to

*G*; since we work with

*F*in the dual space, the approximation to

*F*is taken at a point that

*mirrors*the dual variable.

The algorithm was initially designed for the problem of image reconstruction with spectral CT (computed tomography) imaging, and is implemented for the CT problem in the paper:

- An algorithm for constrained one-step inversion of spectral CT data.

Rina Foygel Barber, Emil Y. Sidky, Taly Gilat Schmidt, Xiaochuan Pan. arXiv:1511.03384

Here are some slides presenting work from both papers.