Consider the problem of minimizing an expected logarithmic loss over either the probability
simplex or the set of quantum density matrices. This problem includes tasks such as solving …

For minimizing a sum of finitely many proper, convex and lower semicontinuous functions
over a nonempty closed convex set in an Euclidean space we propose a stochastic …

The number of measurement outcomes in positron emission tomography (PET) is typically
large, rendering signal reconstruction computationally expensive. We propose an online …

Recent advances in statistical learning and convex optimization have inspired many
successful practices. Standard theories assume smoothness---bounded gradient, Hessian …