arXiv subjects

Malena I. Español

Publications and source records attributed to Malena I. Español.

10 recordsLinked to original sources

arXiv2026

Wavelet-based multilevel framework for $\ell_1$-regularized image deblurring

Solving large-scale $\ell_1$-regularized image deblurring problems efficiently while preserving sharp edges remains a significant computational challenge. We propose a wavelet-based multilevel framework that embeds three iterative solvers, Iteratively Reweighted Least Squares (IRLS), Split Bregman (SB), and Majorization-Minimization (MM), within a multilevel V-cycle. Discrete wavelet transforms define the interlevel transfer operators, and regularization parameters are selected automatically by Generalized Cross Validation. Two information transfer strategies are introduced and compared: one transfers only the coarse solution to the fine level, while the other transfers solver-specific auxiliary quantities. Numerical experiments demonstrate substantial computational savings for IRLS, with speedups exceeding an order of magnitude, while MM and SB exhibit more modest computational differences. The experiments generally show that transferring auxiliary iterates performs best with Haar wavelets, whereas transferring only the solution performs best with Daubechies wavelets.

math.NA↗

arXiv2026

Qualitative reconstruction methods for imaging interior Robin interfaces in EIT from Robin-to-Dirichlet data

We consider an inverse shape problem arising in electrical impedance tomography (EIT) for nondestructive testing, in which interior defects are modeled through Robin transmission conditions. Unlike classical formulations, we impose Robin boundary conditions on both the exterior measurement surface and the interior interface, and use the Robin-to-Dirichlet (RtD) map as the available data. Within this setting, we develop qualitative (non-iterative) reconstruction methods based on the Linear Sampling Method (LSM) and the Regularized Factorization Method (RFM), and derive new analytical characterizations that enable these methods to identify interior regions. We further propose a numerical implementation that incorporates regularization strategies and demonstrate, through experiments, that the methods reliably reconstruct interior regions of interest.

math.NA↗

arXiv2026

Variable Projection Methods for Solving Regularized Separable Inverse Problems with Applications to Semi-Blind Image Deblurring

Separable nonlinear least squares problems appear in many inverse problems, including semi-blind image deblurring. The variable projection (VarPro) method provides an efficient approach for solving such problems by eliminating linear variables and reducing the problem to a smaller, nonlinear one. In this work, we extend VarPro to solve minimization problems containing a differentiable regularization term on the nonlinear parameters, along with a general-form Tikhonov regularization term on the linear variables. Furthermore, we develop a quasi-Newton method for solving the resulting reduced problem, and provide a local convergence analysis under standard smoothness assumptions, establishing conditions for superlinear or quadratic convergence. For large-scale settings, we introduce an inexact LSQR-based variant and prove its local convergence despite inner-solve and Hessian approximations. Numerical experiments on semi-blind deblurring show that parameter regularization prevents degenerate no-blur solutions and that the proposed methods achieve accurate reconstructions, with the inexact variant offering a favorable accuracy-cost tradeoff consistent with the theory.

math.NA↗

arXiv2024

Efficient Decomposition-Based Algorithms for $\ell_1$-Regularized Inverse Problems with Column-Orthogonal and Kronecker Product Matrices

We consider an $\ell_1$-regularized inverse problem where both the forward and regularization operators have a Kronecker product structure. By leveraging this structure, a joint decomposition can be obtained using generalized singular value decompositions. We show how this joint decomposition can be effectively integrated into the Split Bregman and Majorization-Minimization methods to solve the $\ell_1$-regularized inverse problem. Furthermore, for cases involving column-orthogonal regularization matrices, we prove that the joint decomposition can be derived directly from the singular value decomposition of the system matrix. As a result, we show that framelet and wavelet operators are efficient for these decomposition-based algorithms in the context of $\ell_1$-regularized image deblurring problems.

math.NA↗

arXiv2024

Convergence Analysis of a Variable Projection Method for Regularized Separable Nonlinear Inverse Problems

Variable projection methods prove highly efficient in solving separable nonlinear least squares problems by transforming them into a reduced nonlinear least squares problem, typically solvable via the Gauss-Newton method. When solving large-scale separable nonlinear inverse problems with general-form Tikhonov regularization, the computational demand for computing Jacobians in the Gauss-Newton method becomes very challenging. To mitigate this, iterative methods, specifically LSQR, can be used as inner solvers to compute approximate Jacobians. This article analyzes the impact of these approximate Jacobians within the variable projection method and introduces stopping criteria to ensure convergence. We also present numerical experiments where we apply the proposed method to solve a blind deconvolution problem to illustrate and confirm our theoretical results.

math.NA↗

arXiv2023

Box-Constrained $L_1/L_2$ Minimization in Single-View Tomographic Reconstruction

We present a note on the implementation and efficacy of a box-constrained $L_1/L_2$ regularization in numerical optimization approaches to performing tomographic reconstruction from a single projection view. The constrained $L_1/L_2$ minimization problem is constructed and solved using the Alternating Direction Method of Multipliers (ADMM). We include brief discussions on parameter selection and numerical convergence, as well as detailed numerical demonstrations against relevant alternative methods. In particular, we benchmark against a box-constrained TVmin and an unconstrained Filtered Backprojection in both cone and parallel beam (Abel) forward models. We consider both a fully synthetic benchmark, and reconstructions from X-ray radiographic image data.

math.OC↗

arXiv2023

Aggregation Phenomena in Lyotropic Chromonic Liquid Crystals

We study the aggregation phenomenon in lyotropic chromonic liquid crystals as the molecular concentration changes and condensing agents are added into the system. Using properties of the critical points of the Oseen-Frank energy of a nematic liquid crystal, combined with the geometric constraints of the hexagonal columnar chromonic phases, we show that the minimizers of the total energy are topologically equivalent to tori, in agreement with available experimental evidence on chromonic liquid crystals and DNA condensates, in viral capsids as well as in free solution. We model the system as bi-phasic, consisting of liquid crystal molecules and water, and postulate the total energy as the sum of the Flory-Huggins energy of mixing together with the bending and surface tension contributions of the liquid crystal. Two types of problems are considered, one related to finding the optimal shape of a torus, once the phase separation has occurred, and the second one that models the conditions leading to molecular aggregation. This work follows recent experimental investigations, but without addressing the topological properties of the toroidal nuclei observed and focusing on how the liquid crystal order competes with the aggregation phenomenon.

cond-mat.soft↗

arXiv2022

The Image Deblurring Problem: Matrices, Wavelets, and Multilevel Methods

The image deblurring problem consists of reconstructing images from blur and noise contaminated available data. In this AMS Notices article, we provide an overview of some well known numerical linear algebra techniques that are use for solving this problem. In particular, we start by carefully describing how to represent images, the process of blurring an image and modeling different kind of added noise. Then, we present regularization methods such as Tikhonov (on the standard and general form), Total Variation and other variations with sparse and edge preserving properties. Additionally, we briefly overview some of the main matrix structures for the blurring operator and finalize presenting multilevel methods that preserve such structures. Numerical examples are used to illustrate the techniques described.

math.NA↗

arXiv2017

A Discrete-to-Continuum Model of Weakly Interacting Incommensurate Two-Dimensional Lattices

In this paper we propose a continuum variational model for a two-dimensional deformable lattice of atoms interacting with a two-dimensional rigid lattice. The two lattices have slightly different lattice parameters and there is a small relative rotation between them. This is a prototypical example of a three-dimensional system consisting of a graphene sheet suspended over a substrate. The continuum model recovers both qualitatively and quantitatively the behavior observed in the corresponding discrete model. The continuum model predicts that the deformable lattice develops a network of domain walls characterized by large shearing, stretching, and bending deformation that accommodate the misalignment and/or mismatch between the deformable and rigid lattices. Two integer-valued parameters that can be identified with the components of a Burgers vector, describe the mismatch between the lattices and determine the geometry and the detail of deformation associated with the domain walls.

cond-mat.mtrl-sci↗

arXiv2014

Optimal Regularization Parameters for General-Form Tikhonov Regularization

In this work we consider the problem of finding optimal regularization parameters for general-form Tikhonov regularization using training data. We formulate the general-form Tikhonov solution as a spectral filtered solution using the generalized singular value decomposition of the matrix of the forward model and a given regularization matrix. Then, we find the optimal regularization parameter by minimizing the average of the errors between the filtered solutions and the true data. We extend the approach to the multi-parameter Tikhonov problem for the case where all the matrices involved are simultaneously diagonalizable. For problems where this is not the case, we describe an approach to compute optimal or near-optimal regularization parameters by using operator approximations for the original problem. Several tests are performed for 1D and 2D examples using different norms on the errors, showing the effectiveness of this approach.

math.NA↗