  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.
