A framework for interprocedural optimization in the presence of dynamic class loading
PLDI '00 Proceedings of the ACM SIGPLAN 2000 conference on Programming language design and implementation
Efficient Java RMI for parallel programming
ACM Transactions on Programming Languages and Systems (TOPLAS)
The Mathematica Guidebook: Programming with CD-ROM
The Mathematica Guidebook: Programming with CD-ROM
Advanced Engineering Mathematics: Maple Computer Guide
Advanced Engineering Mathematics: Maple Computer Guide
Core Java 2, Volume 1: Fundamentals
Core Java 2, Volume 1: Fundamentals
Constructing Intelligent Agents Using Java
Constructing Intelligent Agents Using Java
Technology Reviews: 3Ms for Instruction: Reviews of Maple, Mathematica, and Matlab
Computing in Science and Engineering
Matlab Guide
3Ms for Instruction, Part 2: Maple, Mathematica, and Matlab
Computing in Science and Engineering
Introduction to Neural Networks with Java
Introduction to Neural Networks with Java
Modeling and Simulation in Scilab/Scicos with ScicosLab 4.4
Modeling and Simulation in Scilab/Scicos with ScicosLab 4.4
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The jLab environment provides a Scilab like scripting language that is executed by an interpreter implemented in the Java language. This language supports all the basic programming constructs and an extensive set of built in mathematical routines that cover all the basic numerical analysis tasks. The efficiency of the Java compiled code can be directly utilized for any computationally intensive operations. Since jLab is coded in pure Java the build from source process is much cleaner, faster, platform independent and less error prone than similar C/C++/Fortran based open source environments (e.g. Scilab, Octave).