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jLab: Integrating a scripting interpreter with Java technology for flexible and efficient scientific computation

机译:jLab:将脚本解释器与Java技术集成在一起,以实现灵活高效的科学计算

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The jLab environment extends the potential of Java for scientific computing. It provides a Matlab/Scilab like scripting language that is executed by an interpreter implemented in the Java language. The scripting language supports the basic programming constructs with Matlab like matrix manipulation operators. The jLab "core" provides the general purpose functionality with an extensive set of built in mathematical routines that cover all the basic numerical analysis tasks.rnApplication specific functionality can be dynamically "plugged" in jLab by means of toolboxes. These toolboxes can be easily implemented in Java. They are packaged in jar files for convenient handling and their corresponding classes can be dynamically integrated to the system.rnThe important advantage of jLab compared to other similar environments is the potentiality to dynamically and automatically integrate Java code to the system in order to obtain both execution speed and to reduce the programming effort. This task is supported both by an easy to use extension Java class wizard and by application specific class wizards that automate the utilization of jLab's scientific libraries.rnNumerical analysis algorithms can require enormous computation resources and at the same time an expressive programming environment. We demonstrate the potentiality of jLab by describing the implementation of a simple numerical analysis algorithm that detects the zero of a function. Also an additional example concerning the solution of ODEs with the compute intensive Runge-Kutta methods is illustrated. The former task can be facilitated with the external class wizard while the ODE wizard can completely automate the later.
机译:jLab环境扩展了Java在科学计算中的潜力。它提供了类似于Matlab / Scilab的脚本语言,该语言由以Java语言实现的解释器执行。脚本语言支持使用Matlab的基本编程结构,例如矩阵操作运算符。 jLab“核心”为通用功能提供了广泛的内置数学例程集,涵盖了所有基本的数值分析任务。可以通过工具箱将特定于应用程序的功能动态“插入” jLab。这些工具箱可以用Java轻松实现。它们打包在jar文件中以方便处理,并且它们的相应类可以动态地集成到系统中。与其他类似环境相比,jLab的重要优势在于可以动态自动地将Java代码集成到系统中以同时获得执行速度并减少编程工作量。易于使用的扩展Java类向导和可自动利用jLab科学库的特定于应用程序的类向导均支持此任务。数值分析算法可能需要大量的计算资源,同时还需要富有表现力的编程环境。我们通过描述检测函数零的简单数值分析算法的实现来证明jLab的潜力。还说明了有关使用计算密集型Runge-Kutta方法求解ODE的其他示例。外部类向导可以简化前一个任务,而ODE向导可以完全自动化后者。

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