My current study shows that with context approac

Question

< breandan> My current study shows that with context approach we can get all abstractions free in performance because everything is resolved statically The only question is boxing and it seems like I found a way around it For now it works only on JVM because I need ByteBuffer for it but with development of kotlinx io we can move it to other platforms as well

breandan · Answer

Interesting I ll take a closer look at kmath looks like it s heading in the right direction I ve been looking for a good JVM tensor math library and there are a few implementations out there e g but I ve been wondering if it s possible to build something in Kotlin for while and what additional features a Kotlin native library could offer

altavir · Answer

I am not sure it is good yet Performance on basic operations is OK but my pythonic students are not happy with the feature set So I need some input about the direction of next steps Also linear algebra currently is almost non existent

altavir · Answer

Because I actually do not use it regular geometry is more important for me

breandan · Answer

Not necessarily looking for stability but a more principled approach in terms of type safety I m interested in applying it to automatic differentiation and would like to avoid writing a bunch of messy code to translate from mathematical notation to numerical types This is my side project <https github com breandan kotlingrad>

breandan · Answer

I m working on some algebraic abstractions for numerical types but really this should be a separate project altogether <https github com breandan kotlingrad tree master src main kotlin co ndan kotlingrad math algebra>

altavir · Answer

It seems I ve already done that in the previous version before kmath inside larger project As for kmath it features expression building exactly for that purpose You can see it here <https github com altavir kmath blob dev kmath core src commonMain kotlin scientifik kmath expressions Expression kt>

altavir · Answer

And tests are here <https github com altavir kmath blob dev kmath core src commonTest kotlin scientifik kmath expressions FieldExpressionContextTest kt>

altavir · Answer

For automatic differentiation we need only to build alternative `ExpressionContext`

breandan · Answer

Interesting It looks very close to my use case I ve been using property testing from KotlinTest

breandan · Answer

These are my tests <https github com breandan kotlingrad blob master src test kotlin co ndan kotlingrad calculus TestFiniteDifferences kt>

altavir · Answer

We currently have people to develop it but I need further insight in what people actually need and feature requests I also would be very happy for any contributions It is obvious that mathematic library should be developed by community not single person

altavir · Answer

By the way current Kmath structure in theory allows some more complicated things like AD in complex numbers or even ND structures everything that forms a `Field` or `ExtendedField` if we need trigonometry etc

breandan · Answer

Agreed I don t have enough resources to do it properly but maybe if we pooled our resources on kmath I ll think of some use cases and get back to you Yeah this is interesting I like the idea of abstracting over different number systems I think if done properly you could provide an embedded DSL for symbolic representation of mathematical expressions where numerical types like `Double` `Float` `Int` were just instances of a generic type

altavir · Answer

Indeed We need more performance tests but for now optimized things work faster than in python single core

altavir · Answer

I mean numpy of course