Convergence Results for Primal-Dual Algorithms in the Presence of Adjoint Mismatch

Most optimization problems arising in imaging science involve high-dimensional linear operators and their adjoints. In the implementations of these operators, approximations may be introduced for various practical considerations (e.g., memory limitation, computational cost, convergence speed), leading to an adjoint mismatch. This occurs for the X-ray tomographic inverse problems found in Computed Tomography (CT), where the … Read more

Unmatched Preconditioning of the Proximal Gradient Algorithm

This works addresses the resolution of penalized least-squares problems using the proximal gradient algorithm (PGA). It is known that PGA can be accelerated by preconditioning strategies. However, typical effective choices of preconditioners may correspond to intricate matrices that are not easily inverted, and lead to an increased complexity in the computation of the proximity step. … Read more

Convergence Analysis of Block Majorize-Minimize Subspace Approaches

Majorization-Minimization (MM) consists of a class of efficient and effective optimization algorithms that benefit from solid theoretical foundations. MM methods have shown their great ability to tackle efficiently challenging optimization problems from signal processing, image processing, inverse problems and machine learning. When processing large amount of data/variable, as it may happen in 3D image processing, … Read more

A Local MM Subspace Method for Solving Constrained Variational Problems in Image Recovery

This article introduces a new Penalized Majorization-Minimization Subspace algorithm (P-MMS) for solving smooth, constrained optimization problems. In short, our approach consists of embedding a subspace algorithm in an inexact exterior penalty procedure. The subspace strategy, combined with a Majoration-Minimization step-size search, takes great advantage of the smoothness of the penalized cost function, while the penalty … Read more

SABRINA: A Stochastic Subspace Majorization-Minimization Algorithm

A wide class of problems involves the minimization of a coercive and differentiable function $F$ on $\mathbb{R}^N$ whose gradient cannot be evaluated in an exact manner. In such context, many existing convergence results from standard gradient-based optimization literature cannot be directly applied and robustness to errors in the gradient is not necessarily guaranteed. This work … Read more

Convergence of Proximal Gradient Algorithm in the Presence of Adjoint Mismatch

We consider the proximal gradient algorithm for solving penalized least-squares minimization problems arising in data science. This first-order algorithm is attractive due to its flexibility and minimal memory requirements allowing to tackle large-scale minimization problems involving non-smooth penalties. However, for problems such as X-ray computed tomography, the applicability of the algorithm is dominated by the … Read more

A Proximal Interior Point Algorithm with Applications to Image Processing

In this article, we introduce a new proximal interior point algorithm (PIPA). This algorithm is able to handle convex optimization problems involving various constraints where the objective function is the sum of a Lipschitz differentiable term and a possibly nonsmooth one. Each iteration of PIPA involves the minimization of a merit function evaluated for decaying … Read more

General risk measures for robust machine learning

A wide array of machine learning problems are formulated as the minimization of the expectation of a convex loss function on some parameter space. Since the probability distribution of the data of interest is usually unknown, it is is often estimated from training sets, which may lead to poor out-of-sample performance. In this work, we … Read more

Deep Unfolding of a Proximal Interior Point Method for Image Restoration

Variational methods are widely applied to ill-posed inverse problems for they have the ability to embed prior knowledge about the solution. However, the level of performance of these methods significantly depends on a set of parameters, which can be estimated through computationally expensive and time-consuming methods. In contrast, deep learning offers very generic and efficient … Read more

Proximal Approaches for Matrix Optimization Problems: Application to Robust Precision Matrix Estimation.

In recent years, there has been a growing interest in mathematical mod- els leading to the minimization, in a symmetric matrix space, of a Bregman di- vergence coupled with a regularization term. We address problems of this type within a general framework where the regularization term is split in two parts, one being a spectral … Read more