maple，mathematica和matlab的不同（英语）

feiyuzhen

         Matlab started from a suite of numerical algorithms and was developed for computational engineering applications. It assumes that the user will need to develop computational skills to work effectively. Its strength lies in programming numerical computations, and it has an engineering look and feel, extending from its development interface’s functionality to its workflow organization. It has a very large array of specialty add-on’s (or tool boxes), reflecting its large presence in the professional engineering workplace. Its working environment—the same for both developers and users—is very close to that of standard code development systems built for third-generation languages such as C++ and Fortran. It will seem most natural to those who are accustomed to programming and are willing to give up standard mathematical notation and facile interactivity in the standard user interface.

         Mathematica, arguably, started from an effort to systematize mathematical computing. It has a unique and rather exotic concept of primitives—in effect, nudging users away from conventional ways of thinking about computing toward a possibly more valuable, yet more demanding, mathematical view. For example, Mathematica’s computational primitive is the expression, which it uses to represent everything conceptually—not just mathematical formulas, but also lists and graphics. Mathematica uses the list as a primitive data type, even for matrices. This economy of primitives affects more than just internal implementations of algorithms. As our earlier examples showed, it also influences the external representations of mathematical objects—notations and vocabulary. Mathematica challenges users to think systematically about mathematical computing, albeit within a landscape that might seem somewhat alien to conventional scientists and engineers.

          Maple, in contrast, favors mathematical convention. It started from an effort to make symbolic algebraic computing available and accessible to the broad academic community. In its notation and vocabulary, it leverages the user’s familiarity with mathematical convention rather than trying to reshape it. This manifests itself in a dominant personality trait—simplification. Nowhere is this more evident than in the way it displays its tools to emphasize their relevant mathematical structures. Maple’s vector and matrix construction templates (on the tool palette), for example, distinguish between row and column vectors, and between (n′ m) and (m′ n) matrices. Many shortcuts in its command vocabulary and the implicit assumptions it uses in interpreting input are common to mathematical analyses.


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