Scientific computing requires handling large linear models, which are often composed of structured matrices. With increasing model size, dense representations quickly become infeasible to compute or store. Matrix-free implementations are suited to mitigate this problem but usually complicate research and development effort by months, when applied to practical research problems. Fastmat is a framework for handling large composed or structured matrices by offering an easy-to-use abstraction model. It allows expressing and using linear operators in a mathematically intuitive way, while maintaining a strong focus on efficient computation and memory storage. The implemented user interface allows for very readable code implementation with very close relationship to the actual mathematical notation of a given problem. Further it provides means for quickly testing new implementations and also allows for run-time execution path optimization. Summarizing, fastmat provides a flexible and extensible framework for handling matrix-free linear structured operators efficiently, while being intuitive and generating easy-to-reuse results.
翻译:科学计算需要处理大型线性模型,这些模型通常由结构化矩阵组成。随着模型规模的扩大,密集的表示迅速变得无法进行计算或存储。无矩阵执行适合缓解这一问题,但在应用到实际研究问题时,通常使研发工作按月复杂。Fastmat是一个处理大型成份或结构化矩阵的框架,它提供了一种容易使用的抽象模型。它允许以数学直观的方式表达和使用线性操作员,同时保持对高效计算和内存存储的强烈关注。已安装的用户界面允许非常可读的代码执行,与一个特定问题的实际数学符号非常密切地相关。它还提供了快速测试新执行手段,并允许运行执行路径优化。 Summart, 快mat为高效处理无矩阵线性结构操作员提供了一个灵活和可扩展的框架,同时保持直观性并产生容易使用的结果。