An optimal sixteenth order family of methods for solving nonlinear equations and their basins of attraction

We propose a new family of iterative methods for finding the simple roots of nonlinear equation. The proposed method is four-point method with convergence order 16, which consists of four steps: the Newton step, an optional fourth order iteration scheme, an optional eighth order iteration scheme and the step constructed using the divided difference. By reason of the new iteration scheme requiring four function evaluations and one first derivative evaluation per iteration, the method satisfies the optimality criterion in the sense of Kung-Traub\u27s conjecture and achieves a high efficiency index $16^{1/5} approx 1.7411$. Computational results support theoretical analysis and confirm the efficiency. The basins of attraction of the new presented algorithms are also compared to the existing methods with encouraging results

Optimal iterative methods for finding multiple roots of nonlinear equations using weight functions and dynamics

[EN] In this paper, we propose a family of iterative methods for finding multiple roots, with known multiplicity, by means of the introduction of four univariate weight functions. With the help of these weight functions, that play an important role in the development of higher order convergent iterative techniques, we are able to construct three-point eight-order optimal multiple-root finders. Also, numerical experiments have been applied to a number of test equations for different special schemes from this family satisfying the conditions given in the convergence analysis. We have also compared the basins of attraction of some proposed and known methods in order to check the wideness of the sets of converging initial points for each problem. (C) 2018 Elsevier B.V. All rights reserved.This research was partially supported by Ministerio de Economia y Competitividad, Spain MTM2014-52016-C2-2-P, MTM2015-64013-P and Generalitat Valenciana, Spain PROMETEO/2016/089 and Schlumberger Foundation-Faculty for Future Program.Zafar, F.; Cordero Barbero, A.; Sultana, S.; Torregrosa Sánchez, JR. (2018). Optimal iterative methods for finding multiple roots of nonlinear equations using weight functions and dynamics. Journal of Computational and Applied Mathematics. 342:352-374. https://doi.org/10.1016/j.cam.2018.03.033S35237434

High Performance Multidimensional Iterative Processes for Solving Nonlinear Equations

[ES] En gran cantidad de problemas de la matemática aplicada, existe la necesidad de resolver ecuaciones y sistemas no lineales, dado que numerosos problemas, finalmente, se reducen a estos. Conforme aumenta la dificultad de los sistemas, la obtención de la solución analítica se vuelve más compleja. Además, con el aumento de las herramientas computacionales, las dimensiones de los problemas a resolver han crecido de manera exponencial, por lo que se vuelve más necesario obtener una aproximación a la solución de manera sencilla y que no requiera mucho tiempo y coste computacional. Esta es una de las razones por las que los métodos iterativos han aumentado su importancia en los últimos años, ya que se han diseñado multitud de procesos con el fin de que converjan rápidamente a la solución y, de esta forma, poder resolver problemas que con las herramientas clásicas resultaría más costoso. La presente Tesis Doctoral, se centra en estudiar y diseñar numerosos métodos iterativos que mejoren a los esquemas clásicos en cuanto a su orden de convergencia, accesibilidad, cantidad de soluciones que obtienen o aplicabilidad a problemas con características especiales, como la no diferenciabilidad o la multiplicidad de las raíces. Entre los procesos que se estudian en esta memoria, se pueden encontrar desde una familia de métodos multipaso óptimos para la resolución de ecuaciones, hasta una familia paramétrica libre de derivadas de esquemas con función peso a la que se introduce memoria para la resolución de sistemas no lineales. Se destacan otros métodos en esta memoria como esquemas iterativos que obtienen raíces con diversas multiplicidades para ecuaciones y procesos que aproximan raíces de forma simultánea, tanto para ecuaciones como para sistemas, y, tanto para raíces simples como para múltiples. Además, parte de esta memoria se centra en cómo realizar el análisis dinámico para métodos iterativos con memoria que resuelven sistemas de ecuaciones no lineales, a la par que se realiza dicho estudio para diversos esquemas iterativos conocidos. Este análisis dinámico permite visualizar y analizar los posibles comportamientos de los procesos iterativos en función de las aproximaciones iniciales. Los resultados anteriormente descritos forman parte de esta Tesis Doctoral para la obtención del título de Doctora en Matemáticas.[CA] En gran quantitat de problemes de la matemàtica aplicada, existeix la necessitat de resoldre equacions i sistemes no lineals, atés que nombrosos problemes, finalment, es redueixen a aquests. Conforme augmenta la dificultat dels sistemes, l'obtenció de la solució analítica es torna més complexa. A més, amb l'augment de les eines computacionals, les dimensions dels problemes a resoldre han crescut de manera exponencial, per la qual cosa es torna més necessari obtindre una aproximació a la solució de manera senzilla i que no requerisca molt temps i cost computacional. Aquesta és una de les raons per les quals els mètodes iteratius han augmentat la seua importància en els últims anys, ja que s'han dissenyat multitud de processos amb la finalitat que convergisquen ràpidament a la solució i, d'aquesta manera, poder resoldre problemes que amb les eines clàssiques resultaria més costós. La present Tesi Doctoral, es centra en estudiar i dissenyar nombrosos mètodes iteratius que milloren als esquemes clàssics en quant al seu ordre de convergència, accessibilitat, quantitat de solucions que obtenen o aplicabilitat a problemes amb característiques especials, com la no diferenciabilitat o la multiplicitat de les arrels. Entre els processos que s'estudien en aquesta memòria, es poden trobar des d'una família de mètodes multipas òptims per a la resolució d'equacions, fins a una família paramètrica lliure de derivades de esquemes amb funció pes a la que s'introdueix memòria per a la resolució de sistemes no lineals. Es destanquen altres mètodes en aquesta memòria com esquemes iteratius que obtenen arrels amb diverses multiplicitats per a equacions i processos que aproximen arrels de manera simultània, tant per a equacions com per a sistemes, i, tant per a arrels simples com per a múltiples. A més, part d'aquesta memòria es centra en com realitzar l'anàlisi dinàmic per a mètodes iteratius amb memòria que resolen sistemes d'equacions no lineals, al mateix temps que es realitza aquest estudi per a diversos esquemes iteratius coneguts. Aquest anàlisi dinàmic permet visualitzar i analitzar els possibles comportaments dels mètodes iteratius en funció de les aproximacions inicials. Els resultats anteriorment descrits formen part d'aquesta Tesi Doctoral per a l'obtenció del títol de Doctora en Matemàtiques.[EN] In a large number of problems in applied mathematics, there is a need to solve nonlinear equations and systems, since many problems eventually are reduced to these. As the difficulty of the systems increases, obtaining the analytical solution becomes more complex. Furthermore, with the growth of computational tools, the dimensions of the problems to be solved have increased exponentially, making it more essential to obtain an approximation to the solution in a simple way that does not require significant time and computational cost. That is one of the reasons why iterative methods have increased their importance in recent years, as a multitude of schemes have been designed to converge rapidly to the solution and, in this way, to be able to solve problems that would be more arduous to solve using classical tools. This Doctoral Thesis focuses on the study and design of numerous iterative methods that improve classical schemes in terms of their order of convergence, accessibility, number of solutions obtained or applicability to problems with special characteristics, such as non-differentiability or multiplicity of roots. The procedures studied in this report range from a family of optimal multi-step methods for solving equations, to a parametric derivative-free family of weight function schemes, to which memory is introduced for solving nonlinear systems. Additional procedures are described in this report such as iterative schemes that obtain roots with different multiplicities for equations and methods that approximate roots simultaneously for equations as well as for systems, and for simple as well as for multiples roots. In addition, part of this report focuses on how to perform the dynamical analysis for iterative schemes with memory that solve systems of nonlinear equations, as well as this study is carried out for different known iterative procedures. This dynamical analysis allows us to visualise and analyse the possible behaviours of the iterative methods depending on the initial approximations. The results described above form part of this Doctoral Thesis to obtain the title of Doctor in Mathematics.Triguero Navarro, P. (2023). High Performance Multidimensional Iterative Processes for Solving Nonlinear Equations [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/19426

Optimising Spatial and Tonal Data for PDE-based Inpainting

Some recent methods for lossy signal and image compression store only a few selected pixels and fill in the missing structures by inpainting with a partial differential equation (PDE). Suitable operators include the Laplacian, the biharmonic operator, and edge-enhancing anisotropic diffusion (EED). The quality of such approaches depends substantially on the selection of the data that is kept. Optimising this data in the domain and codomain gives rise to challenging mathematical problems that shall be addressed in our work. In the 1D case, we prove results that provide insights into the difficulty of this problem, and we give evidence that a splitting into spatial and tonal (i.e. function value) optimisation does hardly deteriorate the results. In the 2D setting, we present generic algorithms that achieve a high reconstruction quality even if the specified data is very sparse. To optimise the spatial data, we use a probabilistic sparsification, followed by a nonlocal pixel exchange that avoids getting trapped in bad local optima. After this spatial optimisation we perform a tonal optimisation that modifies the function values in order to reduce the global reconstruction error. For homogeneous diffusion inpainting, this comes down to a least squares problem for which we prove that it has a unique solution. We demonstrate that it can be found efficiently with a gradient descent approach that is accelerated with fast explicit diffusion (FED) cycles. Our framework allows to specify the desired density of the inpainting mask a priori. Moreover, is more generic than other data optimisation approaches for the sparse inpainting problem, since it can also be extended to nonlinear inpainting operators such as EED. This is exploited to achieve reconstructions with state-of-the-art quality. We also give an extensive literature survey on PDE-based image compression methods

Computational methods and software systems for dynamics and control of large space structures

Two key areas of crucial importance to the computer-based simulation of large space structures are discussed. The first area involves multibody dynamics (MBD) of flexible space structures, with applications directed to deployment, construction, and maneuvering. The second area deals with advanced software systems, with emphasis on parallel processing. The latest research thrust in the second area involves massively parallel computers

The Magnus expansion and some of its applications

Approximate resolution of linear systems of differential equations with varying coefficients is a recurrent problem shared by a number of scientific and engineering areas, ranging from Quantum Mechanics to Control Theory. When formulated in operator or matrix form, the Magnus expansion furnishes an elegant setting to built up approximate exponential representations of the solution of the system. It provides a power series expansion for the corresponding exponent and is sometimes referred to as Time-Dependent Exponential Perturbation Theory. Every Magnus approximant corresponds in Perturbation Theory to a partial re-summation of infinite terms with the important additional property of preserving at any order certain symmetries of the exact solution. The goal of this review is threefold. First, to collect a number of developments scattered through half a century of scientific literature on Magnus expansion. They concern the methods for the generation of terms in the expansion, estimates of the radius of convergence of the series, generalizations and related non-perturbative expansions. Second, to provide a bridge with its implementation as generator of especial purpose numerical integration methods, a field of intense activity during the last decade. Third, to illustrate with examples the kind of results one can expect from Magnus expansion in comparison with those from both perturbative schemes and standard numerical integrators. We buttress this issue with a revision of the wide range of physical applications found by Magnus expansion in the literature.Comment: Report on the Magnus expansion for differential equations and its applications to several physical problem

Closed-loop control of an open cavity flow using reduced-order models

International audienceThe control of separated fluid flow by reduced-order models is studied using the two-dimensional incompressible flow over an open square cavity at Reynolds numbers where instabilities are present. Actuation and measurement locations are taken on the upstream and downstream edge of the cavity. A bi-orthogonal projection is introduced to arrive at reduced-order models for the compensated problem. Global modes, proper orthogonal decomposition (POD) modes and balanced modes are used as expansion bases for the model reduction. The open-loop behaviour of the full and the reduced systems is analysed by comparing the respective transfer functions. This analysis shows that global modes are inadequate to sufficiently represent the inputoutput behaviour whereas POD and balanced modes are capable of properly approximating the exact transfer function. Balanced modes are far more efficient in this process, but POD modes show superior robustness. The performance of the closed-loop system corroborates this finding: while reduced-order models based on POD are able to render the compensated system stable, balanced modes accomplish the same with far fewer degrees of freedom. © 2009 Cambridge University Press