All issues

We present the iterative algorithm that solves numerically both Urysohn type Fredholm and Volterra nonlinear one-dimensional nonsingular integral equations of the second kind to a specified, modest user-defined accuracy. The algorithm is based on descending recursive sequence of quadratures. Convergence of numerical scheme is guaranteed by fixed-point theorems. Picard’s method of integrating successive approximations is of great importance for the existence theory of integral equations but surprisingly very little appears on numerical algorithms for its direct implementation in the literature. We show that successive approximations method can be readily employed in numerical solution of integral equations. By that the quadrature algorithm is thoroughly designed. It is based on the explicit form of fifth-order embedded Runge–Kutta rule with adaptive step-size self-control. Since local error estimates may be cheaply obtained, continuous monitoring of the quadrature makes it possible to create very accurate automatic numerical schemes and to reduce considerably the main drawback of Picard iterations namely the extremely large amount of computations with increasing recursion depth. Our algorithm is organized so that as compared to most approaches the nonlinearity of integral equations does not induce any additional computational difficulties, it is very simple to apply and to make a program realization. Our algorithm exhibits some features of universality. First, it should be stressed that the method is as easy to apply to nonlinear as to linear equations of both Fredholm and Volterra kind. Second, the algorithm is equipped by stopping rules by which the calculations may to considerable extent be controlled automatically. A compact C++-code of described algorithm is presented. Our program realization is self-consistent: it demands no preliminary calculations, no external libraries and no additional memory is needed. Numerical examples are provided to show applicability, efficiency, robustness and accuracy of our approach.

We consider a finite-dimensional optimization problem, the formulation of which in addition to the required variables contains parameters. The solution to this problem is a dependence of optimal values of variables on parameters. In general, these dependencies are not functions because they can have ambiguous meanings and in the functional case be nondifferentiable. In addition, their domain of definition may be narrower than the domains of definition of functions in the condition of the original problem. All these properties make it difficult to solve both the original parametric problem and other tasks, the statement of which includes these dependencies. To overcome these difficulties, usually methods such as non-differentiable optimization are used.

This article proposes an alternative approach that makes it possible to obtain solutions to parametric problems in a form devoid of the specified properties. It is shown that such representations can be explored using standard algorithms, based on the Taylor formula. This form is a function smoothly approximating the solution of the original problem for any parameter values, specified in its statement. In this case, the value of the approximation error is controlled by a special parameter. Construction of proposed approximations is performed using special functions that establish feedback (within optimality conditions for the original problem) between variables and Lagrange multipliers. This method is described for linear problems with subsequent generalization to the nonlinear case.

From a computational point of view the construction of the approximation consists in finding the saddle point of the modified Lagrange function of the original problem. Moreover, this modification is performed in a special way using feedback functions. It is shown that the necessary conditions for the existence of such a saddle point are similar to the conditions of the Karush – Kuhn – Tucker theorem, but do not contain constraints such as inequalities and conditions of complementary slackness. Necessary conditions for the existence of a saddle point determine this approximation implicitly. Therefore, to calculate its differential characteristics, the implicit function theorem is used. The same theorem is used to reduce the approximation error to an acceptable level.

Features of the practical implementation feedback function method, including estimates of the rate of convergence to the exact solution are demonstrated for several specific classes of parametric optimization problems. Specifically, tasks searching for the global extremum of functions of many variables and the problem of multiple extremum (maximin-minimax) are considered. Optimization problems that arise when using multicriteria mathematical models are also considered. For each of these classes, there are demo examples.

One of the numerical methods for solving problems of scattering of electromagnetic waves by systems formed by parallel oriented cylindrical elements — two-dimensional photonic crystals — is considered. The method is based on the classical method of separation of variables for solving the wave equation. Тhe essence of the method is to represent the field as the sum of the primary field and the unknown secondary scattered on the elements of the medium field. The mathematical expression for the latter is written in the form of infinite series in elementary wave functions with unknown coefficients. In particular, the field scattered by N elements is sought as the sum of N diffraction series, in which one of the series is composed of the wave functions of one body, and the wave functions in the remaining series are expressed in terms of the eigenfunctions of the first body using addition theorems. From satisfying the boundary conditions on the surface of each element we obtain systems of linear algebraic equations with an infinite number of unknowns — the required expansion coefficients, which are solved by standard methods. A feature of the method is the use of analytical expressions describing diffraction by a single element of the system. In contrast to most numerical methods, this approach allows one to obtain information on the amplitude-phase or spectral characteristics of the field only at local points of the structure. The absence of the need to determine the field parameters in the entire area of space occupied by the considered multi-element system determines the high efficiency of this method. The paper compares the results of calculating the transmission spectra of two-dimensional photonic crystals by the considered method with experimental data and numerical results obtained using other approaches. Their good agreement is demonstrated.

The article is devoted to studying the application of convex optimization methods to solve the Cauchy problem for the Helmholtz equation, which is ill-posed since the equation belongs to the elliptic type. The Cauchy problem is formulated as an inverse problem and is reduced to a convex optimization problem in a Hilbert space. The functional to be optimized and its gradient are calculated using the solution of boundary value problems, which, in turn, are well-posed and can be approximately solved by standard numerical methods, such as finite-difference schemes and Fourier series expansions. The convergence of the applied fast gradient method and the quality of the solution obtained in this way are experimentally investigated. The experiment shows that the accelerated gradient method — the Similar Triangle Method — converges faster than the non-accelerated method. Theorems on the computational complexity of the resulting algorithms are formulated and proved. It is found that Fourier’s series expansions are better than finite-difference schemes in terms of the speed of calculations and improve the quality of the solution obtained. An attempt was made to use restarts of the Similar Triangle Method after halving the residual of the functional. In this case, the convergence does not improve, which confirms the absence of strong convexity. The experiments show that the inaccuracy of the calculations is more adequately described by the additive concept of the noise in the first-order oracle. This factor limits the achievable quality of the solution, but the error does not accumulate. According to the results obtained, the use of accelerated gradient optimization methods can be the way to solve inverse problems effectively.

Monkeypox is a disease reemerging in 2022 which is caused by the monkeypox virus (MPV). This disease can be transmitted not only from rodents to humans, but also from humans to other humans, and even from the environment to humans. In this work, we propose a mathematical model to capture the dynamics of monkeypox transmission which involve three modes of transmission, namely, from rodents to rodents, rodents to humans, and from humans to other humans. In addition to the basic reproduction number, we investigate the stability of all equilibrium points analytically, including an implicit endemic equilibrium by applying the center manifold theorem. Moreover, the vaccination as an alternative solution to eradicate the monkeypox transmission is discussed and solved as an optimal control problem. The results of this study show that the transmission of monkeypox is directly affected by the internal infection rates of each population, i. e., the infection rate of the susceptible human by an infected human and the infection rate of the susceptible rodent by an infected rodent. Furthermore, the external infection rates, i. e., the infection rate of the susceptible human by an infected rodent also affects the transmission of monkeypox although it does not affect the basic reproduction number directly.