2017/10/26 by Christoph Wagner, Wagner, Christoph Wilfried, Sebastian Semper +2
Computer Science · Physics and Astronomy · #Computational Physics and Python Applications #FOS: Computer and information sciences #Mathematical Software (cs.MS) #Numerical Methods and Algorithms #Scientific Research and Discoveries
paper · pdf · doi:10.48550/arxiv.1710.09578
openalex publication_date 2017/10/26 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.