The search for best approximate solutions, considering some reasonablebounds of errors, imposes tight accuracy requirements on the computational platforms and its libraries or functions. To illustrate this, when a finite element mesh is used to perform a structural analysis, computing matrix inversions and determinants are important commonplace operations which are rarely checked. The final output is the result of a myriad of often disregarded intermediate computations. Many mathematical models which arise in diverse areas as engineering, bioinformatics and ecology rely on partial differential equations (PDE) or ordinary differential equations (ODE), where the high number of variables requires strong computational effort in their solution. Mathematical modelling aims at solving complex problems which can be described in a rigorous mathematical way that enables the use of computers for finding the solution. Key words: numerical analisys, computational platforms, spectral graph analysis, statistical computing. Mathematical subject classification: Primary: 06B10 Secondary: 06D05. The assessment was made comparing the results computed by the platforms with certified values, that is, known results, computing the number of correct significant digits. We also used data provided by NIST (National Institute of Standards and Technology), a protocol which includes the computation of basic univariate statistics (mean, standard deviation and first-lag correlation), linear regression and extremes of probability distributions. We propose a set of operations which include the computation of matrix determinants and eigenvalues, whose results are known. A Monte Carlo study was conducted in some of the datasets in order to verify the stability of the results with respect to small departures from the original input. We submitted them to numerical tests using standard data sets and using the functions provided by each platform.
#Octave vs matlab for mac mac
E-mails: / / this article we test the accuracy of three platforms used in computational modelling: MatLab, Octave and Scilab, running on i386 architecture and three operating systems (Windows, Ubuntu and Mac OS).
Universidade Federal de Alagoas, BR 104 Norte Km 97, 57072-970 Maceió, AL, Brazil. Centro de Pesquisas em Matemática Computacional (CPMAT). Laboratório de Computação Científica e Análise Numérica (LaCCAN).
How good are MatLab, Octave and Scilab for computational modelling?Įliana S. Numerical analisys computational platforms spectral graph analysis statistical computing
In this article we test the accuracy of three platforms used in computational modelling: MatLab, Octave and Scilab, running on i386 architecture and three operating systems (Windows, Ubuntu and Mac OS).
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