Relevant bibliographies by topics / Mathematical and software

Academic literature on the topic 'Mathematical and software'

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Published: 30 May 2022
Last updated: 31 May 2022

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Journal articles on the topic "Mathematical and software"

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Argon, E., I. L. Chang, G. Gunaratna, D. K. Kahaner, and M. A. Reed. "Mathematical software: Plod." IEEE Micro 8, no. 4 (August 1988): 56–61. http://dx.doi.org/10.1109/40.7772.

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Krogh, Fred T. "On developing mathematical software." Journal of Computational and Applied Mathematics 185, no. 2 (January 2006): 196–202. http://dx.doi.org/10.1016/j.cam.2005.03.005.

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Hake, J. Fr. "Mathematical software at KFA." ACM SIGNUM Newsletter 20, no. 2 (April 1985): 20–30. http://dx.doi.org/10.1145/1057941.1057945.

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Williams, Donald L. "A mathematical software environment." ACM SIGNUM Newsletter 21, no. 3 (July 1986): 2–12. http://dx.doi.org/10.1145/1057958.1057959.

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Gayoso Martínez, Víctor, Luis Hernández Encinas, Agustín Martín Muñoz, and Araceli Queiruga Dios. "Using Free Mathematical Software in Engineering Classes." Axioms 10, no. 4 (October 12, 2021): 253. http://dx.doi.org/10.3390/axioms10040253.

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There are many computational applications and engines used in mathematics, with some of the best-known arguably being Maple, Mathematica, MATLAB, and Mathcad. However, although they are very complete and powerful, they demand the use of commercial licences, which can be a problem for some education institutions or in cases where students desire to use the software on an unlimited number of devices or to access it from several of them simultaneously. In this contribution, we show how GeoGebra, WolframAlpha, Python, and SageMath can be applied to the teaching of different mathematical courses in engineering studies, as they are some of the most interesting representatives of free (and mostly open source) mathematical software. As the best way to show a topic in mathematics is by providing examples, this article explains how to make calculations for some of the main topics associated with Calculus, Algebra, and Coding theories. In addition to this, we provide some results associated with the usage of Mathematica in different graded activities. Moreover, the comparison between the results from students that use Mathematica and students that participate in a “traditional” course, solving problems and attending to master classes, is shown.
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Ochkov, Valery, and Elena Bogomolova. "Teaching Mathematics with Mathematical Software." Journal of Humanistic Mathematics 5, no. 1 (January 2015): 265–85. http://dx.doi.org/10.5642/jhummath.201501.15.

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Wallis, P. "Mathematical Structures for Software Engineering." Computer Journal 35, no. 1 (February 1, 1992): 80. http://dx.doi.org/10.1093/comjnl/35.1.80.

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Dongarra, J., and E. Grosse. "Shopping for mathematical software electronically." IEEE Potentials 8, no. 1 (February 1989): 37–38. http://dx.doi.org/10.1109/45.31582.

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Lucks, Michael, and Ian Gladwell. "Automated selection of mathematical software." ACM Transactions on Mathematical Software 18, no. 1 (March 1992): 11–34. http://dx.doi.org/10.1145/128745.128747.

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Chatterjee, Samprit, Ronald F. Boisvert, Sally E. Howe, and David K. Kahaner. "Guide to Available Mathematical Software." Journal of the American Statistical Association 80, no. 392 (December 1985): 1082. http://dx.doi.org/10.2307/2288608.

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Dissertations / Theses on the topic "Mathematical and software"

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Olsson, Jan. "Dynamic software enhancing creative mathematical reasoning." Licentiate thesis, Umeå universitet, Institutionen för naturvetenskapernas och matematikens didaktik, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-90285.

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This thesis includes two articles and a coat. The articles present two studies investigating students’ reasoning when they were working in pairs, solving mathematics problems using the dynamic software, GeoGebra, The first study shows that the students used GeoGebra as a collaborative environment where they shared their individual reasoning to one another. Furthermore, GeoGebra provided the students with feedback that, to some extent, became a basis for their creative reasoning. The second study looked more closely into the relation between students’ reasoning and their utilization of the feedback generated by GeoGebra. The study showed that students who before entering computer commands used creative mathematical reasoning to hypothesize what the outcome may be, understood the feedback from software better and used it more efficiently. The students who engaged in imitative reasoning were mainly able to use feedback to determine if a solution attempt was correct or not, but did not fully understand the feedback and were less able to use it to make further progress in solving the task. The coat explains theories and methodologies more thoroughly and discusses the results of the two articles. In a concluding discussion the results of the articles are linked and possible implications for teaching are proposed. In school it is common that teachers and textbooks provide students with algorithmic solution templates to tasks, but in the study the didactic situation with dynamic software was found to invite students to create their own solution methods. Furthermore the thesis suggests that it could be beneficial for the students to be encouraged to pay more attention to their own solving strategies, i.e. to explain and evaluate their methods and results rather than merely looking for the correct answers.
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Chang, Tyler Hunter. "Mathematical Software for Multiobjective Optimization Problems." Diss., Virginia Tech, 2020. http://hdl.handle.net/10919/98915.

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In this thesis, two distinct problems in data-driven computational science are considered. The main problem of interest is the multiobjective optimization problem, where the tradeoff surface (called the Pareto front) between multiple conflicting objectives must be approximated in order to identify designs that balance real-world tradeoffs. In order to solve multiobjective optimization problems that are derived from computationally expensive blackbox functions, such as engineering design optimization problems, several methodologies are combined, including surrogate modeling, trust region methods, and adaptive weighting. The result is a numerical software package that finds approximately Pareto optimal solutions that are evenly distributed across the Pareto front, using minimal cost function evaluations. The second problem of interest is the closely related problem of multivariate interpolation, where an unknown response surface representing an underlying phenomenon is approximated by finding a function that exactly matches available data. To solve the interpolation problem, a novel algorithm is proposed for computing only a sparse subset of the elements in the Delaunay triangulation, as needed to compute the Delaunay interpolant. For high-dimensional data, this reduces the time and space complexity of Delaunay interpolation from exponential time to polynomial time in practice. For each of the above problems, both serial and parallel implementations are described. Additionally, both solutions are demonstrated on real-world problems in computer system performance modeling.
Doctor of Philosophy
Science and engineering are full of multiobjective tradeoff problems. For example, a portfolio manager may seek to build a financial portfolio with low risk, high return rates, and minimal transaction fees; an aircraft engineer may seek a design that maximizes lift, minimizes drag force, and minimizes aircraft weight; a chemist may seek a catalyst with low viscosity, low production costs, and high effective yield; or a computational scientist may seek to fit a numerical model that minimizes the fit error while also minimizing a regularization term that leverages domain knowledge. Often, these criteria are conflicting, meaning that improved performance by one criterion must be at the expense of decreased performance in another criterion. The solution to a multiobjective optimization problem allows decision makers to balance the inherent tradeoff between conflicting objectives. A related problem is the multivariate interpolation problem, where the goal is to predict the outcome of an event based on a database of past observations, while exactly matching all observations in that database. Multivariate interpolation problems are equally as prevalent and impactful as multiobjective optimization problems. For example, a pharmaceutical company may seek a prediction for the costs and effects of a proposed drug; an aerospace engineer may seek a prediction for the lift and drag of a new aircraft design; or a search engine may seek a prediction for the classification of an unlabeled image. Delaunay interpolation offers a unique solution to this problem, backed by decades of rigorous theory and analytical error bounds, but does not scale to high-dimensional "big data" problems. In this thesis, novel algorithms and software are proposed for solving both of these extremely difficult problems.
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Vasylenko, Oleksii, Viktor Chuprynka, and Natalia Chuprynka. "Mathematical software for automated gloves design." Thesis, Київський національний університет технологій та дизайну, 2021. https://er.knutd.edu.ua/handle/123456789/19096.

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Mwangi, Timothy M. "Software tools for elementary math education : animated mathematical proofs." Thesis, Massachusetts Institute of Technology, 2013. http://hdl.handle.net/1721.1/85451.

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Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2013.
Cataloged from PDF version of thesis.
Includes bibliographical references (page 47).
The National Council of Teachers of Mathematics [6] has identified the learning of proofs as a critical goal for students from pre-kindergarten through grade 12 (p. 56). A proof for elementary students is not the highly structured mathematical argument seen in high school algebra classes. It is, however, a rational mathematical argument created by students using the appropriate vocabulary for their level of understanding. To aid students in learning to create mathematical proofs software that enables them to create simple animations is invaluable. This thesis looks at the characteristics, design, testing and evaluation of such software. An initial design is presented and the feedback gained from testing its implementation in a class setting is discussed along with the changes that were required to improve the software in light of the feedback. A comparison is then made between the final implementation of the software and other similar programs. The results indicate that the software enables students to create, share and discuss mathematical proofs in the form of simple animations.
by Timothy M. Mwangi.
M. Eng.
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Smith, Barbara Mary. "Bus crew scheduling using mathematical programming." Thesis, University of Leeds, 1986. http://etheses.whiterose.ac.uk/1053/.

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This thesis describes a bus crew scheduling system, IMPACS, which has been demonstrated to be successful for a wide variety of scheduling conditions, and is at present in regular use by three British bus companies including the largest, London Buses Ltd. The background to the bus crew scheduling problem 1S described and the existing literature on methods for solution is reviewed. In IMPACS, the crew scheduling problem is formulated as an integer linear programme using a formulation which is an extension of set covering; a very large set of possible duties is generated, from which the duties forming the schedule are selected in such a way as to minimise the total cost. The variables of the set covering problem correspond to the duties generated and the constraints to the pieces of work in the bus schedule. For realistic schedules, it is impossible to generate all legal duties, and there are often too many pieces of work to allow each one to give rise to a constraint. IMPACS contains several heuristic methods which reduce the set covering problem to a manageable size, while still allowing good quality schedules to be compiled. Techniques for speeding up the solution of the set covering problem have been investigated, and in particular a branching strategy which exploits features of the crew scheduling problem has been developed.
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Giuliani, Giulia. "Analysis and improvement of a software framework for solving mathematical puzzles." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2019.

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I giochi matematici costituiscono un ampio e popolare campo, richiedendo differenti livelli di capacità ed abilità per risolvere i diversi problemi. Sono usati ogni giorno nelle scuole per allenare gli studenti, per spronarli nell'applicazione delle loro conoscenze. Se pensassimo i sistemi informatici come un giovane, che ha bisogno di imparare e migliorare le sue abilità, potremmo poi applicar loro lo stesso tipo di allenamento. Con quest’idea, in questo lavoro ci focalizzeremo sull'analisi dei diversi tipi di giochi matematici che le competizioni per giovani studenti offrono, capendo le differenti categorie e le loro caratteristiche. Approfondiremo anche i problemi legati all'analisi delle immagini, poiché nella risoluzione è molto importante la corretta comprensione dei dati di input, un compito che diventa più impegnativo nel momento in cui dobbiamo confrontarci con diversi tipi di fonti. Continuando ad analizzare questo percorso, analizzeremo come l’elaborazione del testo e dei diagrammi funzioni singolarmente, in modo da poter poi alla fine modellare i puzzle usando la combinazione di questi dati. L'NLP è il campo collegato all'elaborazione delle informazioni testuali, mentre per le immagini partiremo da un lavoro esistente, provando a migliorarlo e incrementare le funzionalità che offre. Il lavoro è quindi strutturato in 3 diverse aree: • Riorganizzazione e miglioramento dell’esistente framework, rendendolo più user friendly e andando a colmare alcune mancanze nei predicati per l’analisi dell’immagine. • Sviluppo del middle layer compreso tra l'NLP del testo e la definizione del modello del problema, mostrando come il modello stesso sia costruito, partendo dai dati iniziali. • Sviluppo di una web application che combina tutti i lavori, in modo da rendere disponibile agli utenti uno strumento per la risoluzione di giochi matematici, offrendo inoltre la possibilità di personalizzare il problema selezionato.
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Ferris, Michael Charles. "Weak sharp minima and penalty functions in mathematical programming." Thesis, University of Cambridge, 1988. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.292969.

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Yau, Shuk-Han Ada. "Numerical analysis of finite difference schemes in automatically generated mathematical modeling software." Thesis, Massachusetts Institute of Technology, 1994. http://hdl.handle.net/1721.1/35407.

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Thesis (M.S.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1994.
Includes bibliographical references (leaves 64-65).
by Shuk-Han Ada Yau.
M.S.
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Gill, Mandeep Singh. "Application of software engineering methodologies to the development of mathematical biological models." Thesis, University of Oxford, 2013. http://ora.ox.ac.uk/objects/uuid:35178f3a-7951-4f1c-aeab-390cdd622b05.

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Mathematical models have been used to capture the behaviour of biological systems, from low-level biochemical reactions to multi-scale whole-organ models. Models are typically based on experimentally-derived data, attempting to reproduce the observed behaviour through mathematical constructs, e.g. using Ordinary Differential Equations (ODEs) for spatially-homogeneous systems. These models are developed and published as mathematical equations, yet are of such complexity that they necessitate computational simulation. This computational model development is often performed in an ad hoc fashion by modellers who lack extensive software engineering experience, resulting in brittle, inefficient model code that is hard to extend and reuse. Several Domain Specific Languages (DSLs) exist to aid capturing such biological models, including CellML and SBML; however these DSLs are designed to facilitate model curation rather than simplify model development. We present research into the application of techniques from software engineering to this domain; starting with the design, development and implementation of a DSL, termed Ode, to aid the creation of ODE-based biological models. This introduces features beneficial to model development, such as model verification and reproducible results. We compare and contrast model development to large-scale software development, focussing on extensibility and reuse. This work results in a module system that enables the independent construction and combination of model components. We further investigate the use of software engineering processes and patterns to develop complex modular cardiac models. Model simulation is increasingly computationally demanding, thus models are often created in complex low-level languages such as C/C++. We introduce a highly-efficient, optimising native-code compiler for Ode that generates custom, model-specific simulation code and allows use of our structured modelling features without degrading performance. Finally, in certain contexts the stochastic nature of biological systems becomes relevant. We introduce stochastic constructs to the Ode DSL that enable models to use Stochastic Differential Equations (SDEs), the Stochastic Simulation Algorithm (SSA), and hybrid methods. These use our native-code implementation and demonstrate highly-efficient stochastic simulation, beneficial as stochastic simulation is highly computationally intensive. We introduce a further DSL to model ion channels declaratively, demonstrating the benefits of DSLs in the biological domain. This thesis demonstrates the application of software engineering methodologies, and in particular DSLs, to facilitate the development of both deterministic and stochastic biological models. We demonstrate their benefits with several features that enable the construction of large-scale, reusable and extensible models. This is accomplished whilst providing efficient simulation, creating new opportunities for biological model development, investigation and experimentation.
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Motiwala, Quaeed. "Optimizations for acyclic dataflow graphs for hardware-software codesign." Thesis, This resource online, 1994. http://scholar.lib.vt.edu/theses/available/etd-06302009-040504/.

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Books on the topic "Mathematical and software"

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Bigatti, Anna Maria, Jacques Carette, James H. Davenport, Michael Joswig, and Timo de Wolff, eds. Mathematical Software – ICMS 2020. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-52200-1.

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Hong, Hoon, and Chee Yap, eds. Mathematical Software – ICMS 2014. Berlin, Heidelberg: Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-662-44199-2.

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Iglesias, Andrés, and Nobuki Takayama, eds. Mathematical Software - ICMS 2006. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11832225.

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Fukuda, Komei, Joris van der Hoeven, Michael Joswig, and Nobuki Takayama, eds. Mathematical Software – ICMS 2010. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-15582-6.

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Greuel, Gert-Martin, Thorsten Koch, Peter Paule, and Andrew Sommese, eds. Mathematical Software – ICMS 2016. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-42432-3.

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Davenport, James H., Manuel Kauers, George Labahn, and Josef Urban, eds. Mathematical Software – ICMS 2018. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-96418-8.

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Mathematical software tools in C++. Chichester: John Wiley, 1993.

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Albert, Walter G. Mathematical and statistical software index. 2nd ed. Brooks Air Force Base, Tex: Air Force Human Resources Laboratory, Air Force Systems Command, 1986.

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Ehrig, Hartmut, Christiane Floyd, Maurice Nivat, and James Thatcher, eds. Mathematical Foundations of Software Development. Berlin, Heidelberg: Springer Berlin Heidelberg, 1985. http://dx.doi.org/10.1007/3-540-15198-2.

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Rice, J. R., ed. Mathematical Aspects of Scientific Software. New York, NY: Springer New York, 1988. http://dx.doi.org/10.1007/978-1-4684-7074-1.

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Book chapters on the topic "Mathematical and software"

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Schittkowski, Klaus. "Mathematical Optimization." In Software Systems for Structural Optimization, 33–42. Basel: Birkhäuser Basel, 1993. http://dx.doi.org/10.1007/978-3-0348-8553-9_2.

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Chrapary, Hagen, and Wolfgang Dalitz. "Software Products, Software Versions, Archiving of Software, and swMATH." In Mathematical Software – ICMS 2018, 123–27. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-96418-8_15.

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Chung, Youngjoo. "Symbolic Computing Package for Mathematica for Versatile Manipulation of Mathematical Expressions." In Mathematical Software – ICMS 2014, 21–25. Berlin, Heidelberg: Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-662-44199-2_4.

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Schittkowski, K. "Software for Mathematical Programming." In Computational Mathematical Programming, 383–451. Berlin, Heidelberg: Springer Berlin Heidelberg, 1985. http://dx.doi.org/10.1007/978-3-642-82450-0_14.

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van der Hoeven, Joris. "Mathematical Font Art." In Mathematical Software – ICMS 2016, 522–29. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-42432-3_67.

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Sitnikovski, Boro. "Mathematical Induction." In Introducing Software Verification with Dafny Language, 77–83. Berkeley, CA: Apress, 2022. http://dx.doi.org/10.1007/978-1-4842-7978-6_7.

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Sitnikovski, Boro. "Mathematical Foundations." In Introducing Software Verification with Dafny Language, 37–46. Berkeley, CA: Apress, 2022. http://dx.doi.org/10.1007/978-1-4842-7978-6_4.

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England, Matthew. "Machine Learning for Mathematical Software." In Mathematical Software – ICMS 2018, 165–74. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-96418-8_20.

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Schulz-Reese, M. "Software-Oriented Mathematical Continuing Education." In European Consortium for Mathematics in Industry, 337–40. Wiesbaden: Vieweg+Teubner Verlag, 1992. http://dx.doi.org/10.1007/978-3-663-09834-8_70.

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Rice, John R. "Mathematical Aspects of Scientific Software." In Mathematical Aspects of Scientific Software, 1–39. New York, NY: Springer New York, 1988. http://dx.doi.org/10.1007/978-1-4684-7074-1_1.

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Conference papers on the topic "Mathematical and software"

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Williams, Donald L. "A Mathematical Software Environment." In the 1986 workshop. New York, New York, USA: ACM Press, 1986. http://dx.doi.org/10.1145/800239.807164.

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DEWAR, MIKE, and DAVID CARLISLE. "FROM MATHEMATICAL SERVERS TO MATHEMATICAL SERVICES." In Proceedings of the First International Congress of Mathematical Software. WORLD SCIENTIFIC, 2002. http://dx.doi.org/10.1142/9789812777171_0045.

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Rice, J. R. "Mathematical software and ACM Publications." In the ACM conference. New York, New York, USA: ACM Press, 1987. http://dx.doi.org/10.1145/41579.41584.

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Maibaum, Tom. "Mathematical foundations of software engineering." In the conference. New York, New York, USA: ACM Press, 2000. http://dx.doi.org/10.1145/336512.336548.

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Huang Chang-feng, He Lun-zhi, and Han Zhao-xiu. "The application of Mathematical software." In 2010 2nd International Conference on Information Science and Engineering (ICISE). IEEE, 2010. http://dx.doi.org/10.1109/icise.2010.5689666.

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Oplachko, E. S., S. D. Rykunov, and M. N. Ustinin. "Encephalography data handling software." In Mathematical Biology and Bioinformatics. Pushchino: IMPB RAS - Branch of KIAM RAS, 2018. http://dx.doi.org/10.17537/icmbb18.90.

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BORWEIN, JONATHAN MICHAEL. "THE EXPERIMENTAL MATHEMATICIAN: A COMPUTATIONAL GUIDE TO THE MATHEMATICAL UNKNOWN." In Proceedings of the First International Congress of Mathematical Software. WORLD SCIENTIFIC, 2002. http://dx.doi.org/10.1142/9789812777171_0001.

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Andreica, Alina, Daniel Stuparu, and Calin Miu. "Applying mathematical models in software design." In 2012 IEEE International Conference on Intelligent Computer Communication and Processing (ICCP). IEEE, 2012. http://dx.doi.org/10.1109/iccp.2012.6356166.

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Hinov, N., D. Vakovsky, G. Kraev, and Michail D. Todorov. "Hybrid Inverter Analysis Using Mathematical Software." In APPLICATIONS OF MATHEMATICS IN ENGINEERING AND ECONOMICS: Proceedings of the 34th Conference on Applications of Mathematics in Engineering and Economics (AMEE '08). AIP, 2008. http://dx.doi.org/10.1063/1.3030825.

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Gallant, Reuven. "Modeling Semantics sans Mathematical Formalism." In 7th International Workshop on Software Knowledge. SCITEPRESS - Science and Technology Publications, 2016. http://dx.doi.org/10.5220/0006098900440054.

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Reports on the topic "Mathematical and software"

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Ribbens, C., and L. Watson. Parallel mathematical software. Office of Scientific and Technical Information (OSTI), October 1989. http://dx.doi.org/10.2172/5587283.

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Shampine, L. F. Mathematical software for ODEs. Office of Scientific and Technical Information (OSTI), January 1989. http://dx.doi.org/10.2172/5441626.

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Hadfield, Steven M., Carl Crockett, Paul J. Simonich, Matthew G. Mcharg, and William J. Mandeville. Mathematical Software Evaluation Report: Mathcad Plus 6.0 versus Mathematica 3.0. Fort Belvoir, VA: Defense Technical Information Center, November 1997. http://dx.doi.org/10.21236/ada337847.

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Boisvert, Ronald F., Sally E. Howe, David K. Kahaner, and Jeanne L. Springmann. Guide to available mathematical software. Gaithersburg, MD: National Institute of Standards and Technology, 1990. http://dx.doi.org/10.6028/nist.ir.90-4237.

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Shampine, L. F. Mathematical software for ODEs. Final technical report. Office of Scientific and Technical Information (OSTI), December 1989. http://dx.doi.org/10.2172/10136708.

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Brackin, Stephen H., and Ian Sutherland. Formal Verification of Mathematical Software. Volume 2. Fort Belvoir, VA: Defense Technical Information Center, May 1990. http://dx.doi.org/10.21236/ada223633.

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Shyshkina, Mariya, Uliana Kohut, and Maiia Popel. The Comparative Analysis of the Cloud-based Learning Components Delivering Access to Mathematical Software. [б. в.], June 2019. http://dx.doi.org/10.31812/123456789/3171.

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In the article, the problems of the systems of computer mathematics use as a tool for the students learning and research activities support are investigated. The promising ways of providing access to the mathematical software in the university learning and research environment are considered. The special aspects of pedagogical applications of these systems to support mathematics and computer science disciplines study in a pedagogical university are considered. The design and evaluation of the cloud-based learning components with the use of the systems of computer mathematics (on the example of the Maxima system and CoCalc) as enchasing the investigative approach to and increasing pedagogical outcomes is justified. The set of psychological and pedagogical and also technological criteria of evaluation is used to compare different approaches to the environment design. The results of pedagogical experiment are provided. The analysis and evaluation of existing experience of mathematical software use both in SaaS and IaaS cloud-based settings is proposed.
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8

Beidler, John. Ada Support for the Mathematical Foundations of Software Engineering. Fort Belvoir, VA: Defense Technical Information Center, November 1993. http://dx.doi.org/10.21236/ada278031.

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9

Boisvert, Ronald F., Sally E. Howe, and Jeanne L. Springmann. Internal structure of the guide to available mathematical software. Gaithersburg, MD: National Institute of Standards and Technology, 1989. http://dx.doi.org/10.6028/nist.ir.89-4042.

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Boisvert, Ronald F., Sally E. Howe, and David K. Kahaner. The guide to available mathematical software problem classification system. Gaithersburg, MD: National Institute of Standards and Technology, 1990. http://dx.doi.org/10.6028/nist.ir.4475.

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