We propose a general yet simple theorem describing the convergence of SGD under the
arbitrary sampling paradigm. Our theorem describes the convergence of an infinite array of …

We propose IntraTomo, a powerful framework that combines the benefits of learning-based
and model-based approaches for solving highly ill-posed inverse problems in the Computed …

In this paper we study several classes of stochastic optimization algorithms enriched with
heavy ball momentum. Among the methods studied are: stochastic gradient descent …

The past decade has seen the rapid growth of model based image reconstruction (MBIR)
algorithms, which are often applications or adaptations of convex optimization algorithms …

We propose a new stochastic first-order algorithmic framework to solve stochastic composite
nonconvex optimization problems that covers both finite-sum and expectation settings. Our …

We present PDLP, a practical first-order method for linear programming (LP) that can solve
to the high levels of accuracy that are expected in traditional LP applications. In addition, it …

Convex nonsmooth optimization problems, whose solutions live in very high dimensional
spaces, have become ubiquitous. To solve them, the class of first-order algorithms known as …

We design accelerated algorithms with improved rates for several fundamental classes of
optimization problems. Our algorithms all build upon techniques related to the analysis of …

We present the Core Imaging Library (CIL), an open-source Python framework for
tomographic imaging with particular emphasis on reconstruction of challenging datasets …

An inexact accelerated stochastic Alternating Direction Method of Multipliers (AS-ADMM)
scheme is developed for solving structured separable convex optimization problems with …