Diophantine Sets. Part II

The article is the next in a series aiming to formalize the MDPR-theorem using the Mizar proof assistant [3], [6], [4]. We analyze four equations from the Diophantine standpoint that are crucial in the bounded quantifier theorem, that is used in one of the approaches to solve the problem.Based on our previous work [1], we prove that the value of a given binomial coefficient and factorial can be determined by its arguments in a Diophantine way. Then we prove that two productsz=∏i=1x(1+i⋅y),        z=∏i=1x(y+1-j),      (0.1)where y > x are Diophantine.The formalization follows [10], Z. Adamowicz, P. Zbierski [2] as well as M. Davis [5].Institute of Informatics, University of Białystok, PolandMarcin Acewicz and Karol Pąk. Basic Diophantine relations. Formalized Mathematics, 26(2):175–181, 2018. doi:10.2478/forma-2018-0015.Zofia Adamowicz and Paweł Zbierski. Logic of Mathematics: A Modern Course of Classical Logic. Pure and Applied Mathematics: A Wiley Series of Texts, Monographs and Tracts. Wiley-Interscience, 1997.Grzegorz Bancerek, Czesław Byliński, Adam Grabowski, Artur Korniłowicz, Roman Matuszewski, Adam Naumowicz, Karol Pąk, and Josef Urban. Mizar: State-of-the-art and beyond. In Manfred Kerber, Jacques Carette, Cezary Kaliszyk, Florian Rabe, and Volker Sorge, editors, Intelligent Computer Mathematics, volume 9150 of Lecture Notes in Computer Science, pages 261–279. Springer International Publishing, 2015. ISBN 978-3-319-20614-1. doi:10.1007/978-3-319-20615-8_17.Grzegorz Bancerek, Czesław Byliński, Adam Grabowski, Artur Korniłowicz, Roman Matuszewski, Adam Naumowicz, and Karol Pąk. The role of the Mizar Mathematical Library for interactive proof development in Mizar. Journal of Automated Reasoning, 61(1):9–32, 2018. doi:10.1007/s10817-017-9440-6.Martin Davis. Hilbert’s tenth problem is unsolvable. The American Mathematical Monthly, Mathematical Association of America, 80(3):233–269, 1973. doi:10.2307/2318447.Adam Grabowski, Artur Korniłowicz, and Adam Naumowicz. Four decades of Mizar. Journal of Automated Reasoning, 55(3):191–198, 2015. doi:10.1007/s10817-015-9345-1.Artur Korniłowicz and Karol Pąk. Basel problem – preliminaries. Formalized Mathematics, 25(2):141–147, 2017. doi:10.1515/forma-2017-0013.Xiquan Liang, Li Yan, and Junjie Zhao. Linear congruence relation and complete residue systems. Formalized Mathematics, 15(4):181–187, 2007. doi:10.2478/v10037-007-0022-7.Karol Pąk. Diophantine sets. Preliminaries. Formalized Mathematics, 26(1):81–90, 2018. doi:10.2478/forma-2018-0007.Craig Alan Smorynski. Logical Number Theory I, An Introduction. Universitext. Springer-Verlag Berlin Heidelberg, 1991. ISBN 978-3-642-75462-3.Tetsuya Tsunetou, Grzegorz Bancerek, and Yatsuka Nakamura. Zero-based finite sequences. Formalized Mathematics, 9(4):825–829, 2001.Rafał Ziobro. On subnomials. Formalized Mathematics, 24(4):261–273, 2016. doi:10.1515/forma-2016-0022.27219720

On the use of stabilization techniques in the Cartesian grid finite element method framework for iterative solvers

"This is the peer reviewed version of the following article: Navarro-Jiménez, José Manuel, Enrique Nadal, Manuel Tur, José Martínez-Casas, and Juan José Ródenas. 2020. "On the Use of Stabilization Techniques in the Cartesian Grid Finite Element Method Framework for Iterative Solvers." International Journal for Numerical Methods in Engineering 121 (13). Wiley: 3004-20. doi:10.1002/nme.6344, which has been published in final form at https://doi.org/10.1002/nme.6344. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Self-Archiving."[EN] Fictitious domain methods, like the Cartesian grid finite element method (cgFEM), are based on the use of unfitted meshes that must be intersected. This may yield to ill-conditioned systems of equations since the stiffness associated with a node could be small, thus poorly contributing to the energy of the problem. This issue complicates the use of iterative solvers for large problems. In this work, we present a new stabilization technique that, in the case of cgFEM, preserves the Cartesian structure of the mesh. The formulation consists in penalizing the free movement of those nodes by a smooth extension of the solution from the interior of the domain, through a postprocess of the solution via a displacement recovery technique. The numerical results show an improvement of the condition number and a decrease in the number of iterations of the iterative solver while preserving the problem accuracy.The authors wish to thank the Spanish "Ministerio de Economía y Competitividad," the "Generalitat Valenciana," and the "Universitat Politècnica de València" for their financial support received through the projects DPI2017-89816-R, Prometeo 2016/007 and the FPI2015 program, respectively.Navarro-Jiménez, J.; Nadal, E.; Tur Valiente, M.; Martínez Casas, J.; Ródenas, JJ. (2020). On the use of stabilization techniques in the Cartesian grid finite element method framework for iterative solvers. International Journal for Numerical Methods in Engineering. 121(13):3004-3020. https://doi.org/10.1002/nme.6344S3004302012113Burman, E., & Hansbo, P. (2010). Fictitious domain finite element methods using cut elements: I. A stabilized Lagrange multiplier method. Computer Methods in Applied Mechanics and Engineering, 199(41-44), 2680-2686. doi:10.1016/j.cma.2010.05.011Ruiz-Gironés, E., & Sarrate, J. (2010). Generation of structured hexahedral meshes in volumes with holes. Finite Elements in Analysis and Design, 46(10), 792-804. doi:10.1016/j.finel.2010.04.005Geuzaine, C., & Remacle, J.-F. (2009). Gmsh: A 3-D finite element mesh generator with built-in pre- and post-processing facilities. International Journal for Numerical Methods in Engineering, 79(11), 1309-1331. doi:10.1002/nme.2579Parvizian, J., Düster, A., & Rank, E. (2007). Finite cell method. Computational Mechanics, 41(1), 121-133. doi:10.1007/s00466-007-0173-yDüster, A., Parvizian, J., Yang, Z., & Rank, E. (2008). The finite cell method for three-dimensional problems of solid mechanics. Computer Methods in Applied Mechanics and Engineering, 197(45-48), 3768-3782. doi:10.1016/j.cma.2008.02.036Nadal, E., Ródenas, J. J., Albelda, J., Tur, M., Tarancón, J. E., & Fuenmayor, F. J. (2013). Efficient Finite Element Methodology Based on Cartesian Grids: Application to Structural Shape Optimization. Abstract and Applied Analysis, 2013, 1-19. doi:10.1155/2013/953786Nadal, E., Ródenas, J. J., Sánchez-Orgaz, E. M., López-Real, S., & Martí-Pellicer, J. (2014). Sobre la utilización de códigos de elementos finitos basados en mallados cartesianos en optimización estructural. Revista Internacional de Métodos Numéricos para Cálculo y Diseño en Ingeniería, 30(3), 155-165. doi:10.1016/j.rimni.2013.04.009Giovannelli, L., Ródenas, J. J., Navarro-Jiménez, J. M., & Tur, M. (2017). Direct medical image-based Finite Element modelling for patient-specific simulation of future implants. Finite Elements in Analysis and Design, 136, 37-57. doi:10.1016/j.finel.2017.07.010Schillinger, D., & Ruess, M. (2014). The Finite Cell Method: A Review in the Context of Higher-Order Structural Analysis of CAD and Image-Based Geometric Models. Archives of Computational Methods in Engineering, 22(3), 391-455. doi:10.1007/s11831-014-9115-yBurman, E., Claus, S., Hansbo, P., Larson, M. G., & Massing, A. (2014). CutFEM: Discretizing geometry and partial differential equations. International Journal for Numerical Methods in Engineering, 104(7), 472-501. doi:10.1002/nme.4823Tur, M., Albelda, J., Marco, O., & Ródenas, J. J. (2015). Stabilized method of imposing Dirichlet boundary conditions using a recovered stress field. Computer Methods in Applied Mechanics and Engineering, 296, 352-375. doi:10.1016/j.cma.2015.08.001Tur, M., Albelda, J., Nadal, E., & Ródenas, J. J. (2014). Imposing Dirichlet boundary conditions in hierarchical Cartesian meshes by means of stabilized Lagrange multipliers. International Journal for Numerical Methods in Engineering, 98(6), 399-417. doi:10.1002/nme.4629De Prenter, F., Verhoosel, C. V., van Zwieten, G. J., & van Brummelen, E. H. (2017). Condition number analysis and preconditioning of the finite cell method. Computer Methods in Applied Mechanics and Engineering, 316, 297-327. doi:10.1016/j.cma.2016.07.006Berger-Vergiat, L., Waisman, H., Hiriyur, B., Tuminaro, R., & Keyes, D. (2011). Inexact Schwarz-algebraic multigrid preconditioners for crack problems modeled by extended finite element methods. International Journal for Numerical Methods in Engineering, 90(3), 311-328. doi:10.1002/nme.3318Menk, A., & Bordas, S. P. A. (2010). A robust preconditioning technique for the extended finite element method. International Journal for Numerical Methods in Engineering, 85(13), 1609-1632. doi:10.1002/nme.3032Dauge, M., Düster, A., & Rank, E. (2015). Theoretical and Numerical Investigation of the Finite Cell Method. Journal of Scientific Computing, 65(3), 1039-1064. doi:10.1007/s10915-015-9997-3Elfverson, D., Larson, M. G., & Larsson, K. (2018). CutIGA with basis function removal. Advanced Modeling and Simulation in Engineering Sciences, 5(1). doi:10.1186/s40323-018-0099-2Verhoosel, C. V., van Zwieten, G. J., van Rietbergen, B., & de Borst, R. (2015). Image-based goal-oriented adaptive isogeometric analysis with application to the micro-mechanical modeling of trabecular bone. Computer Methods in Applied Mechanics and Engineering, 284, 138-164. doi:10.1016/j.cma.2014.07.009Burman, E. (2010). Ghost penalty. Comptes Rendus Mathematique, 348(21-22), 1217-1220. doi:10.1016/j.crma.2010.10.006BadiaS VerdugoF MartínAF. The aggregated unfitted finite element method for elliptic problems;2017.Jomo, J. N., de Prenter, F., Elhaddad, M., D’Angella, D., Verhoosel, C. V., Kollmannsberger, S., … Rank, E. (2019). Robust and parallel scalable iterative solutions for large-scale finite cell analyses. Finite Elements in Analysis and Design, 163, 14-30. doi:10.1016/j.finel.2019.01.009Béchet, É., Moës, N., & Wohlmuth, B. (2008). A stable Lagrange multiplier space for stiff interface conditions within the extended finite element method. International Journal for Numerical Methods in Engineering, 78(8), 931-954. doi:10.1002/nme.2515Hautefeuille, M., Annavarapu, C., & Dolbow, J. E. (2011). Robust imposition of Dirichlet boundary conditions on embedded surfaces. International Journal for Numerical Methods in Engineering, 90(1), 40-64. doi:10.1002/nme.3306Hansbo, P., Lovadina, C., Perugia, I., & Sangalli, G. (2005). A Lagrange multiplier method for the finite element solution of elliptic interface problems using non-matching meshes. Numerische Mathematik, 100(1), 91-115. doi:10.1007/s00211-005-0587-4Burman, E., & Hansbo, P. (2012). Fictitious domain finite element methods using cut elements: II. A stabilized Nitsche method. Applied Numerical Mathematics, 62(4), 328-341. doi:10.1016/j.apnum.2011.01.008Gerstenberger, A., & Wall, W. A. (2008). An eXtended Finite Element Method/Lagrange multiplier based approach for fluid–structure interaction. Computer Methods in Applied Mechanics and Engineering, 197(19-20), 1699-1714. doi:10.1016/j.cma.2007.07.002AxelssonO. Iterative solution methods;1994.Stenberg, R. (1995). On some techniques for approximating boundary conditions in the finite element method. Journal of Computational and Applied Mathematics, 63(1-3), 139-148. doi:10.1016/0377-0427(95)00057-7Zienkiewicz, O. C., & Zhu, J. Z. (1987). A simple error estimator and adaptive procedure for practical engineerng analysis. 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Self-Evaluation Applied Mathematics 2003-2008 University of Twente

This report contains the self-study for the research assessment of the Department of Applied Mathematics (AM) of the Faculty of Electrical Engineering, Mathematics and Computer Science (EEMCS) at the University of Twente (UT). The report provides the information for the Research Assessment Committee for Applied Mathematics, dealing with mathematical sciences at the three universities of technology in the Netherlands. It describes the state of affairs pertaining to the period 1 January 2003 to 31 December 2008

A survey on algorithmic aspects of modular decomposition

The modular decomposition is a technique that applies but is not restricted to graphs. The notion of module naturally appears in the proofs of many graph theoretical theorems. Computing the modular decomposition tree is an important preprocessing step to solve a large number of combinatorial optimization problems. Since the first polynomial time algorithm in the early 70's, the algorithmic of the modular decomposition has known an important development. This paper survey the ideas and techniques that arose from this line of research

Quantitative Verification: Formal Guarantees for Timeliness, Reliability and Performance

Computerised systems appear in almost all aspects of our daily lives, often in safety-critical scenarios such as embedded control systems in cars and aircraft or medical devices such as pacemakers and sensors. We are thus increasingly reliant on these systems working correctly, despite often operating in unpredictable or unreliable environments. Designers of such devices need ways to guarantee that they will operate in a reliable and efficient manner. Quantitative verification is a technique for analysing quantitative aspects of a system's design, such as timeliness, reliability or performance. It applies formal methods, based on a rigorous analysis of a mathematical model of the system, to automatically prove certain precisely specified properties, e.g. ``the airbag will always deploy within 20 milliseconds after a crash'' or ``the probability of both sensors failing simultaneously is less than 0.001''. The ability to formally guarantee quantitative properties of this kind is beneficial across a wide range of application domains. For example, in safety-critical systems, it may be essential to establish credible bounds on the probability with which certain failures or combinations of failures can occur. In embedded control systems, it is often important to comply with strict constraints on timing or resources. More generally, being able to derive guarantees on precisely specified levels of performance or efficiency is a valuable tool in the design of, for example, wireless networking protocols, robotic systems or power management algorithms, to name but a few. This report gives a short introduction to quantitative verification, focusing in particular on a widely used technique called model checking, and its generalisation to the analysis of quantitative aspects of a system such as timing, probabilistic behaviour or resource usage. The intended audience is industrial designers and developers of systems such as those highlighted above who could benefit from the application of quantitative verification,but lack expertise in formal verification or modelling