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In this paper we explore a time-depended SEIR model, in which the dynamics of the infection in four groups from a selected target group (population), divided according to the infection, are modeled by a system of nonlinear ordinary differential equations. Several basic parameters are involved in the model: coefficients of infection rate, incubation rate, recovery rate. The coefficients are adaptable to each specific infection, for each individual country, and depend on the measures to limit the spread of the infection and the effectiveness of the methods of treatment of the infected people in the respective country. If such coefficients are known, solving the nonlinear system is possible to be able to make some hypotheses for the development of the epidemic. This is the reason for using Bulgarian COVID-19 data to first of all, solve the so-called "inverse problem" and to find the parameters of the current situation. Reverse logic is initially used to determine the parameters of the model as a function of time, followed by computer solution of the problem. Namely, this means predicting the future behavior of these parameters, and finding (and as a consequence applying mass-scale measures, e.g., distancing, disinfection, limitation of public events), a suitable scenario for the change in the proportion of the numbers of the four studied groups in the future. In fact, based on these results we model the COVID-19 transmission dynamics in Bulgaria and make a two-week forecast for the numbers of new cases per day, active cases and recovered individuals. Such model, as we show, has been successful for prediction analysis in the Bulgarian situation. We also provide multiple examples of numerical experiments with visualization of the results., Comment: keywords: Nonlinear approximation, Mathematical modeling, data extrapolation

In this paper we consider one particular mathematical problem of this large area of fractional powers of self-adjoined elliptic operators, defined either by Dunford-Taylor-like integrals or by the representation through the spectrum of the elliptic operator. Due to the mathematical modeling of various non-local phenomena using such operators recently a number of numerical methods for solving equations involving operators of fractional order were introduced, studied, and tested. Here we consider the discrete counterpart of such problems obtained from finite difference or finite element approximations of the corresponding elliptic problems. In this report we provide all necessary information regarding the best uniform rational approximation (BURA) $r_{k,\alpha}(t) := P_k(t)/Q_k(t)$ of $t^{\alpha}$ on $[\delta, 1]$ for various $\alpha$, $\delta$, and $k$. The results are presented in 160 tables containing the coefficients of $P_k(t)$ and $Q_k(t)$, the zeros and the poles of $r_{k,\alpha}(t)$, the extremal point of the error $t^\alpha - r_{k,\alpha}(t)$, the representation of $r_{k,\alpha}(t)$ in terms of partial fractions, etc. Moreover, we provide links to the files with the data that characterize $r_{k,\alpha}(t)$ which are available with enough significant digits so one can use them in his/her own computations.

Here we study theoretically and compare experimentally an efficient method for solving systems of algebraic equations, where the matrix comes from the discretization of a fractional diffusion operator. More specifically, we focus on matrices obtained from finite difference or finite element approximation of second order elliptic problems in $\mathbb R^d$, $d=1,2,3$. The proposed methods are based on the best uniform rational approximation (BURA) $r_{\alpha,k}(t)$ of $t^{\alpha}$ on $[0,1]$. Here $r_{\alpha,k}$ is a rational function of $t$ involving numerator and denominator polynomials of degree at most $k$. We show that the proposed method is exponentially convergent with respect to $k$ and has some attractive properties. First, it reduces the solution of the nonlocal system to solution of $k$ systems with matrix $(A +c_j I)$ and $c_j>0$, $j=1,2,\ldots,k$. Thus, good computational complexity can be achieved if fast solvers are available for such systems. Second, the original problem and its rational approximation in the finite difference case are positivity preserving. In the finite element case, positivity preserving results when mass lumping is employed under some mild conditions on the mesh. Further, we prove that in the mass lumping case, the scheme still leads to the expected rate of convergence, at times assuming additional regularity on the right hand side. Finally, we present comprehensive numerical experiments on a number of model problems for various $\alpha$ in one and two spatial dimensions. These illustrate the computational behavior of the proposed method and compare its accuracy and efficiency with that of other methods developed by Harizanov et. al. and Bonito and Pasciak.

We discuss, study, and compare experimentally three methods for solving the system of algebraic equations $\mathbb{A}^\alpha \bf{u}=\bf{f}$, $0< \alpha <1$, where $\mathbb{A}$ is a symmetric and positive definite matrix obtained from finite difference or finite element approximations of second order elliptic problems in $\mathbb{R}^d$, $d=1,2,3$. The first method, introduced by Harizanov et.al, based on the best uniform rational approximation (BURA) $r_\alpha(t)$ of $t^{1-\alpha}$ for $0 \le t \le 1$, is used to get the rational approximation of $t^{-\alpha}$ in the form $t^{-1}r_\alpha(t)$. Here we develop another method, denoted by R-BURA, that is based on the best rational approximation $r_{1-\alpha}(t)$ of $t^\alpha$ on the interval $[0,1]$ and approximates $t^{-\alpha}$ via $r^{-1}_{1-\alpha}(t)$. The third method, introduced and studied by Bonito and Pasciak, is based on an exponentially convergent quadrature scheme for the Dundord-Taylor integral representation of the fractional powers of elliptic operators. All three methods reduce the solution of the system $\mathbb{A}^\alpha \bf{u}=\bf{f}$ to solving a number of equations of the type $(\mathbb{A} +c\mathbb{I})\bf{u}= \bf{f}$, $c \ge 0$. Comprehensive numerical experiments on model problems with $\mathbb A$ obtained by approximation of elliptic equations in one and two spatial dimensions are used to compare the efficiency of these three algorithms depending on the fractional power $\alpha$. The presented results prove the concept of the new R-BURA method, which performs well for $\alpha$ close to $1$ in contrast to BURA, which performs well for $\alpha $ close to $0$. As a result, we show theoretically and experimentally, that they have mutually complementary advantages., Comment: 12 pages, 5 tables, 2 figures

This study is motivated by the recent development in the fractional calculus and its applications. During last few years, several different techniques are proposed to localize the nonlocal fractional diffusion operator. They are based on transformation of the original problem to a local elliptic or pseudoparabolic problem, or to an integral representation of the solution, thus increasing the dimension of the computational domain. More recently, an alternative approach aimed at reducing the computational complexity was developed. The linear algebraic system $\cal A^\alpha \bf u=\bf f$, $0< \alpha <1$ is considered, where $\cal A$ is a properly normalized (scalded) symmetric and positive definite matrix obtained from finite element or finite difference approximation of second order elliptic problems in $\Omega\subset\mathbb{R}^d$, $d=1,2,3$. The method is based on best uniform rational approximations (BURA) of the function $t^{\beta-\alpha}$ for $0 < t \le 1$ and natural $\beta$. The maximum principles are among the major qualitative properties of linear elliptic operators/PDEs. In many studies and applications, it is important that such properties are preserved by the selected numerical solution method. In this paper we present and analyze the properties of positive approximations of $\cal A^{-\alpha}$ obtained by the BURA technique. Sufficient conditions for positiveness are proven, complemented by sharp error estimates. The theoretical results are supported by representative numerical tests.

In this paper we consider efficient algorithms for solving the algebraic equation ${\mathcal A}^\alpha {\bf u}={\bf f}$, $0< \alpha <1$, where ${\mathcal A}$ is a symmetric and positive definite matrix obtained form finite difference or finite element approximations of second order elliptic problems in ${\mathbb R}^d$, $d=1,2,3$. The method is based on the best uniform rational approximation of the function $t^{\beta-\alpha}$ for $0 < t \le 1$ and natural $\beta$, and the assumption that one has at hand an efficient method (e.g. multigrid, multilevel, or other fast algorithm) for solving equations like $({\mathcal A} +c {\mathcal I}){\bf u}= {\bf f}$, $c \ge 0$. The provided numerical experiments on model problems with ${\mathcal A}$ obtained by finite element approximation of elliptic equations in one and three spacial dimensions confirm the efficiency of the proposed algorithms., Comment: 29 pages, 5 figures

Normal multi-scale transform [4] is a nonlinear multi-scale transform for representing geometric objects that has been recently investigated [1, 7, 10]. The restrictive role of the exact order of polynomial reproduction $P_e$ of the approximating subdivision operator $S$ in the analysis of the $S$ normal multi-scale transform, established in [7, Theorem 2.6], significantly disfavors the practical use of these transforms whenever $P_e\ll P$. We analyze in detail the normal multi-scale transform for planar curves based on B-spline subdivision scheme $S_p$ of degree $p\ge3$ and derive higher smoothness of the normal re-parameterization than in [7]. We show that further improvements of the smoothness factor are possible, provided the approximate normals are cleverly chosen. Following [10], we introduce a more general framework for those transforms where more than one subdivision operator can be used in the prediction step, which leads to higher detail decay rates., Comment: Key words and phrases: Nonlinear geometric multi-scale transforms, B-spline subdivision schemes, Lipschitz smoothness, curve representation, detail decay estimates