Academic literature on the topic 'Computational Mathematics'

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Journal articles on the topic "Computational Mathematics"

1

Dean, Walter. "Computational Complexity Theory and the Philosophy of Mathematics†." Philosophia Mathematica 27, no. 3 (2019): 381–439. http://dx.doi.org/10.1093/philmat/nkz021.

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Abstract Computational complexity theory is a subfield of computer science originating in computability theory and the study of algorithms for solving practical mathematical problems. Amongst its aims is classifying problems by their degree of difficulty — i.e., how hard they are to solve computationally. This paper highlights the significance of complexity theory relative to questions traditionally asked by philosophers of mathematics while also attempting to isolate some new ones — e.g., about the notion of feasibility in mathematics, the $\mathbf{P} \neq \mathbf{NP}$ problem and why it has proven hard to resolve, and the role of non-classical modes of computation and proof.
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2

Mezhoud, Salim. "Language Mathematics and Mathematics Language, Reading from Computational Linguistics." Mathematical Linguistics 1, no. 1 (2021): 7–24. http://dx.doi.org/10.58205/ml.v1i1.140.

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The language of mathematics is the system used by mathematicians to communicate mathematical ideas among themselves. This language consists of a substrate of some natural language using technical terms and grammatical conventions that are peculiar to mathematical discourse, supplemented by a highly specialized symbolic notation for mathematical formulas.
 mathematical characterizations of various notions of linguistic complexity include also computational linguistics, philosophical logic, knowledge representation as a branch of artificial intelligence, theoretical computer science, and computational psychology. Mathematical linguistics has initially served as a foundation for computational linguistics, though its research agenda of designing machines to simulate natural language understanding is clearly more applied. Inductive methods have gained the upper hand in applied computational linguistics
 The question is whether mathematics is a language, or that language is mathematical, and how computational linguistics employs language as mathematics.
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3

Azevedo, Greiton Toledo de, Marcus Vinicius Maltempi, and Arthur Belford Powell. "Contexto Formativo de Invenção Robótico-Matemática: Pensamento Computacional e Matemática Crítica." Bolema: Boletim de Educação Matemática 36, no. 72 (2022): 214–38. http://dx.doi.org/10.1590/1980-4415v36n72a10.

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Resumo Neste artigo buscamos identificar e compreender as características do contexto formativo em Matemática de estudantes quando produzem jogos digitais e dispositivos robóticos destinados ao tratamento de sintomas da doença de Parkinson. Norteados pelas ideias da metodologia qualitativa de pesquisa, interagimos com alunos do Ensino Médio visando a construção de um jogo eletrônico com dispositivo robótico, chamado Paraquedas, destinado a sessões de fisioterapia de pacientes com Parkinson. Os alunos foram estimulados a propor e desenvolver ideias em ambientes voltados à experimentação e invenções eletrônicas para beneficiar pessoas em sociedade. Os dados foram analisados à luz dos pressupostos teóricos do Pensamento Computacional e da Matemática Crítica e consistem de discussão-análises do desenvolvimento científico-tecnológico, colaborativo-argumentativo e inventivo-criativo de tecnologias, indo além dos muros da sala de aula de Matemática. Como resultado, identificamos as seguintes características do contexto formativo em Matemática: independência formativa; imprevisibilidade de respostas; aprendizagem centrada na compreensão-investigação-invenção; e conexão entre áreas de conhecimento. Compreendemos que tais características se originam e mutuamente se desenvolvem dinâmico e idiossincraticamente nas concepções de planejamento, diálogo e protagonismo dos sujeitos, os quais fomentam a exploração de problemas aberto e inéditos de Matemática em-uso e descentralizam a formalização excessiva do rigor de objetos matemáticos como ponto nevrálgico à formação em Matemática.
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4

Russell, Susan Jo. "Principles and Standards: Developing Computational Fluency with Whole Numbers." Teaching Children Mathematics 7, no. 3 (2000): 154–58. http://dx.doi.org/10.5951/tcm.7.3.0154.

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Principles and Standards for School Mathematics (NCTM 2000) emphasizes the goal of computational fluency for all students. It articulates expectations regarding fluency with basic number combinations and the importance of computational facility grounded in understanding (see a summary of key messages regarding computation in Principles and Standards in the sidebar on page 156). Building on the Curriculum and Evaluation Standards for School Mathematics (NCTM 1989) and benefiting from a decade of research and practice, Principles and Standards articulates the need for students to develop procedural competence within a school mathematics program that emphasizes mathematical reasoning and problem solving. In fact, learning about whole-number computation is a key context for learning to reason about the baseten number system and the operations of addition, subtraction, multiplication, and division.
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5

Annamalai, Chinnaraji. "Computation and Calculus for Combinatorial Geometric Series and Binomial Identities and Expansions." Journal of Engineering and Exact Sciences 8, no. 7 (2022): 14648–01. http://dx.doi.org/10.18540/jcecvl8iss7pp14648-01i.

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Nowadays, the growing complexity of mathematical and computational modelling demands the simplicity of mathematical and computational equations for solving today’s scientific problems and challenges. This paper presents combinatorial geometric series, innovative binomial coefficients, combinatorial equations, binomial expansions, calculus with combinatorial geometric series, and innovative binomial theorems. Combinatorics involves integers, factorials, binomial coefficients, discrete mathematics, and theoretical computer science for finding solutions to the problems in computing and engineering science. The combinatorial geometric series with binomial expansions and its theorems refer to the methodological advances which are useful for researchers who are working in computational science. Computational science is a rapidly growing multi-and inter-disciplinary area where science, engineering, computation, mathematics, and collaboration use advance computing capabilities to understand and solve the most complex real-life problems.
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6

Abramovich, Sergei. "Computational Triangulation in Mathematics Teacher Education." Computation 11, no. 2 (2023): 31. http://dx.doi.org/10.3390/computation11020031.

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The paper is written to demonstrate the applicability of the notion of triangulation typically used in social sciences research to computationally enhance the mathematics education of future K-12 teachers. The paper starts with the so-called Brain Teaser used as background for (what is called in the paper) computational triangulation in the context of four digital tools. Computational problem solving and problem formulating are presented as two sides of the same coin. By revealing the hidden mathematics of Fibonacci numbers included in the Brain Teaser, the paper discusses the role of computational thinking in the use of the well-ordering principle, the generating function method, digital fabrication, difference equations, and continued fractions in the development of computational algorithms. These algorithms eventually lead to a generalized Golden Ratio in the form of a string of numbers independently generated by digital tools used in the paper.
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7

Matevossian, Hovik A., and Francesco dell’Isola. "“Computational Mathematics and Mathematical Physics”—Editorial I (2021–2023)." Axioms 12, no. 9 (2023): 824. http://dx.doi.org/10.3390/axioms12090824.

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Based on the papers published in the Special Issue of the scientific journal Axioms, here we present the Editorial Article “Computational Mathematics and Mathematical Physics”, the main topics of which include both fundamental and applied research in computational mathematics and differential equations of mathematical physics [...]
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8

Webb, Nigel, M. Beilby, and G. McCauley. "Introduction to Computational Mathematics." Mathematical Gazette 71, no. 457 (1987): 243. http://dx.doi.org/10.2307/3616782.

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9

MTW, Zhongying Chen, Yuesheng Li, Charles A. Micchelli, and Yuesheng Xu. "Advances in Computational Mathematics." Journal of the American Statistical Association 95, no. 450 (2000): 690. http://dx.doi.org/10.2307/2669442.

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10

Fillion, Nicolas. "Conceptual and Computational Mathematics†." Philosophia Mathematica 27, no. 2 (2019): 199–218. http://dx.doi.org/10.1093/philmat/nkz005.

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