in principle there is this low level convergence but differences quickly pile up at the user level. eg armadillo does not support ndarrays except in an indirect

in principle there is this low level convergence but differences quickly pile up at the user level. eg armadillo does not support ndarrays except in an indirect way via "field" objects. 2d and 3d fixed size arrays are in general better supported by all libraries as they have wider applications in viz and games but arbitrary size / dimension tensors are hit and miss (e.g. in eigen they are an "unsupported" extension) it gets even more complicated if you expand into efficient random number generation, graph processing etc. then it becomes important that the library is extensible with some plugin mechanism etc. as, ideally, you don't want your project to be a potpourri of different libraries / API's / dependencies all in all there are some amazing projects out there but sometimes more options means it is harder to decide without spending serious time. I think this is one advantage of the python scipy stack: at the expense of a (possibly small) performance penalty you have one choice only but it is fairly complete and consistent