Research Article10.1007/s11075-026-02364-1Fast computation of best approximation polynomials for the reciprocal functionApr 20, 2026Numerical AlgorithmsGerlind Plonka + 1 more +1CiteListenSave
Research Article10.1007/s11075-026-02363-2Strong convergence, perturbation resilience and superiorization of Generalized Modular String-Averaging with infinitely many input operatorsApr 14, 2026Numerical AlgorithmsKay Barshad + 1 more +1CiteListenSave
Research Article110.1007/s11075-026-02362-3Pass-efficient randomized algorithms for low-rank approximation of quaternion matricesApr 10, 2026Numerical AlgorithmsSalman Ahmadi-Asl + 2 more +2CiteListenSave
Research Article10.1007/s11075-026-02344-5Solving split monotone variational inclusion problems via a relaxed-inertial self-adaptive regularized splitting algorithmMar 07, 2026Numerical AlgorithmsYasir Arfat + 4 more +4CiteListenSave
Research Article10.1007/s11075-026-02325-8RHBMPIA: a new algorithm for computing Hermite-Birkhoff matrix interpolation polynomialsFeb 24, 2026Numerical AlgorithmsOmar Rhouni + 2 more +2CiteListenSave
Research Article10.1007/s11075-026-02332-9Haar wavelets, gradients and approximate total variation regularizationFeb 24, 2026Numerical AlgorithmsTomas Sauer + 1 more +1Abstract Image denoising by means of total variation (TV) regularization is still a standard procedure. For very large images, especially three-dimensional voxel datasets, however, this can be computationally infeasible. We show how this TV regularization can be approximately performed even in arbitrary dimensions by applying appropriate shrinkage to selected and properly weighted Haar wavelet coefficients, all of which depends even on the dimensionality of the data. Our approach acts entirely on the wavelet coefficients which represent the compressed image, and is therefore suited for the application on large three-dimensional images represented in the Haar wavelet basis, e.g., volumes from computed tomography.Read moreCiteListenSave
Research Article10.1007/s11075-025-02300-9On the construction of iterative methods in (p,q)-calculusJan 02, 2026Numerical AlgorithmsSerap Herdem + 1 more +1CiteListenSave
Research Article10.1007/s11075-025-02271-xOn the convergence of some iterative schemes for weak contractions with applications to fractional-order blood flow modelsDec 13, 2025Numerical AlgorithmsSumaira Ajmal + 4 more +4CiteListenSave
Research Article10.1007/s11075-025-02281-9Inertial self-adaptive methods for solving convex bilevel problemsDec 11, 2025Numerical AlgorithmsJiajun Guo + 1 more +1CiteListenSave
Research Article10.1007/s11075-025-02272-wAsymptotic analysis and numerical evaluation of two types of oscillatory hypersingular integralsDec 02, 2025Numerical AlgorithmsHong Wang + 2 more +2CiteListenSave