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We propose a k-space preconditioning formulation for accelerating the convergence of iterative Magnetic Resonance Imaging (MRI) reconstructions from non-uniformly sampled k-space data. Existing methods either use sampling density…
The Method of Moments [Pea94] is one of the most widely used methods in statistics for parameter estimation, by means of solving the system of equations that match the population and estimated moments. However, in practice and especially…
I report on controlled comparison of gravitational approximation schemes linear/lognormal/adhesion/frozen-flow/Zel'dovich(ZA) and ZA's second--order generalization. In the last two cases we also created new versions of the approximation by…
In this paper, we construct a model with the help of modular symmetry in the framework of minimal inverse seesaw [ISS(2,3)]. We have used $\Gamma(3)$ modular group which is isomorphic to non-Abelian discrete symmetry group $A_4$. In this…
We develop a "variational mass" expansion approach, recently introduced in the Gross--Neveu model, to evaluate some of the order parameters of chiral symmetry breakdown in QCD. The method relies on a reorganization of the usual perturbation…
We propose the use of machine learning techniques to find optimal quadrature rules for the construction of stiffness and mass matrices in isogeometric analysis (IGA). We initially consider 1D spline spaces of arbitrary degree spanned over…
In this paper, we introduce an inertial version of the Proximal Incremental Aggregated Gradient method (PIAG) for minimizing the sum of smooth convex component functions and a possibly nonsmooth convex regularization function.…
In this work we demonstrate a surprising way of exploitation of the mosaic--skeleton approximations for efficient numerical solving of aggregation equations with many applied kinetic kernels. The complexity of the evaluation of the…
Motivated by a recent paper by the RBC-UKQCD Collaboration, which observes large violations of the na\"ive factorization hypothesis in $K \to \pi \pi$ decays, we study in this paper the accuracy of the Vacuum Insertion Approximation (VIA)…
In this paper, we give an algorithm that finds an epsilon-approximate solution to a mixed integer quadratic programming (MIQP) problem. The algorithm runs in polynomial time if the rank of the quadratic function and the number of integer…
We consider estimating the parameters of a Gaussian mixture density with a given number of components best representing a given set of weighted samples. We adopt a density interpretation of the samples by viewing them as a discrete Dirac…
Decades of advances in mixed-integer linear programming (MILP) and recent development in mixed-integer second-order-cone programming (MISOCP) have translated very mildly to progresses in global solving nonconvex mixed-integer quadratically…
Method for a mosaic image representation (MIR) is proposed for a selective treatment of image fragments of different transition frequency. MIR method is based on piecewise-constant image approximation on a non-uniform orthogonal grid…
The dynamics of merging black hole binaries with small mass-ratios (SMR) can be expressed as a series in the symmetric mass-ratio, the post-adiabatic expansion. Using numerical relativity simulations, we compute the first three terms in…
In this study, we analyze various Iterative Stockholder Analysis (ISA) methods for molecular density partitioning, focusing on the numerical performance of the recently proposed Linear approximation of Iterative Stockholder Analysis model…
A recently proposed variational mass expansion approach to dynamical chiral symmetry breakdown is reviewed. We briefly explain how a specific integral ansatz over the analytically continued, arbitrary Lagrangian mass parameter, resums the…
Joint equalization and decoding schemes are described for two-dimensional intersymbol interference (ISI) channels. Equalization is performed using the minimum mean-square-error (MMSE) criterion. Low-density parity-check codes are used for…
Near isometric orthogonal embeddings to lower dimensions are a fundamental tool in data science and machine learning. In this paper, we present the construction of such embeddings that minimizes the maximum distortion for a given set of…
Estimating the sharing of genetic effects across different conditions is important to many statistical analyses of genomic data. The patterns of sharing arising from these data are often highly heterogeneous. To flexibly model these…
We revisit the classical problem of estimating an unknown distribution from its samples by fitting a mixture model that minimizes cross-entropy loss. Framing the task as a stochastic convex optimization problem over the space of $ M…