How good are MatLab, Octave and Scilab for Computational Modelling?

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). We submitted them to numerical tests using standard data sets and using the functions provided by each platform. 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 propose a set of operations which include the computation of matrix determinants and eigenvalues, whose results are known. 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. 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.Comment: Accepted for publication in the Computational and Applied Mathematics journa

Analysis of numerical mathematical environments for simulation

En investigación científica y, en particular, en simulación de procesos, MATLAB es el software estándar cuando se requiere un entorno matemático numérico. Sin embargo, el elevado precio de su licencia, el código propietario y la lentitud de cálculo son sus principales debilidades. En este trabajo, se analizan entornos matemáticos numéricos de código abierto y que no requieren el pago de licencias, con el fin de determinar si pueden sustituir a MATLAB en el área de simulación de procesos. Para ello, se recurre a evaluaciones realizadas en trabajos previos y se realiza una evaluación propia usando el modelo de espacio de estados de un caso de estudio. Del análisis realizado, se recomienda a GNU Octave como el mejor reemplazo de MATLAB por su alta compatibilidad y rendimiento. No obstante, también se recomienda el entorno Juno del lenguaje Julia, a pesar de no ser compatible con MATLAB, por presentar excelentes características y alcanzar velocidades comparables a C++.In scientific research and, in particular, in process simulation, MATLAB is the standard software when a numerical mathematical environment is required. However, the high price of its license, the proprietary code and the slow calculation are its main weaknesses. In this work, open source numerical mathematical environments that do not require the payment of licenses are analyzed in order to determine if they can replace MATLAB in the area of process simulation. To do this, evaluations made in previous works are used and an own evaluation is carried out using the state space model of a case study. From the analysis performed, GNU Octave is recommended as the best MATLAB replacement for its high compatibility and performance. Nevertheless, the Juno environment of the Julia language is also recommended, despite not being compatible with MATLAB, for presenting excellent features and reaching speeds comparable to C ++.Fil: Tarifa, Enrique Eduardo. Universidad Nacional de Jujuy. Facultad de Ingeniería; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Salta; ArgentinaFil: Martínez, Sergio Luis. Universidad Nacional de Jujuy. Facultad de Ingeniería; Argentina. Universidad Católica de Santiago del Estero; ArgentinaFil: Franco Domínguez, Samuel. Universidad Nacional de Jujuy. Facultad de Ingeniería; ArgentinaFil: Nuñez, Alvaro Fabian. Universidad Nacional de Jujuy. Facultad de Ingeniería; Argentin

Performance Comparison of Open Source and Commercial Computing Tools in Educational and Other Use — Scilab vs. MATLAB

In this paper, the authors compare the features and the overall performance of the two high-level numerical computing and modeling software environments: the freeware Scilab and commercially available industry-standard MATLAB. The motivation for the work emanated from the educational use of these tools at the college and university level, but with a perspective to their professional and scientific use as well. Their performance is tested by measuring the execution times of several combined-task benchmarks implemented as test functions, built upon nine common numerical tasks that are often found in programs for solving standard engineering problems. They include basic algebra and matrix calculations, signal generation, signal analysis, and storing and retrieving data to and from the hard disk drive. Although MATLAB outperforms Scilab in all the benchmarks except the disk file manipulations, in the presumed vectorization versions of the benchmarks, it is not for much. The overall performance of the freeware rival is very satisfactory, making it a good choice not only for educational use but also for scientific and professional purposes, especially when funding is critical