PyProximal - scalable convex optimization in Python

A broad class of problems in scientific disciplines ranging from image processing and astrophysics, to geophysics and medical imaging call for the optimization of convex, non-smooth objective functions.Whereas practitioners are usually familiar with gradient-based algorithms, commonly used to solve unconstrained, smooth optimization problems, proximal algorithms can be viewed as analogous tools for non-smooth and possibly constrained versions of such problems.These algorithms sit at a higher level of abstraction than gradient-based algorithms and require a basic operation to be performed at each iteration: the evaluation of the so-called proximal operator of the functional to be optimized.PyProximal is a Python-based library aimed at democratizing the application of convex optimization to scientific problems; it provides the required building blocks (i.e., proximal operators and algorithms) to define and solve complex, convex objective functions in a high-level, abstract fashion, shielding users away from any unneeded mathematical and implementation details.