Hello, At Continuum Analytics we've been working on a submodule implementing a set of lineal algebra operations as generalized ufuncs. This allows specifying arrays of lineal algebra problems to be computed with a single Python call, allowing broadcasting as well. As the vectorization is handled in the kernel, this gives a speed edge on the operations. We think this could be useful to the community and we want to share the work done. I've created a couple of pull-requests: The first one contains a fix for a bug in the handling of certain signatures in the gufuncs. This was found while building the submodule. The fix was done by Mark Wiebe, so credit should go to him :). https://github.com/numpy/numpy/pull/2953 The second pull request contains the submodule itself and builds on top of the previous fix. It contains a rst file that explains the submodule, enumerates the functions implemented and details some implementation bits. The entry point to the module is in written in Python and contains detailed docstrings. https://github.com/numpy/numpy/pull/2954 We are open to discussion and to make improvements to the code if needed, in order to adapt to NumPy standards. Thanks, Oscar.