  Blind deconvolution and demixing is the problem of reconstructing convolved
signals and kernels from the sum of their convolutions. This problem arises in
many applications, such as blind MIMO. This work presents a separable approach
to blind deconvolution and demixing via convex optimization. Unlike previous
works, our formulation allows separation into smaller optimization problems,
which significantly improves complexity. We develop recovery guarantees, which
comply with those of the original non-separable problem, and demonstrate the
method performance under several normalization constraints.
