[eigen] General design issue |

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*To*: eigen@xxxxxxxxxxxxxxxxxxx*Subject*: [eigen] General design issue*From*: Gael Guennebaud <gael.guennebaud@xxxxxxxxx>*Date*: Fri, 13 Nov 2009 16:41:25 +0100*Dkim-signature*: v=1; a=rsa-sha256; c=relaxed/relaxed; d=gmail.com; s=gamma; h=domainkey-signature:mime-version:received:date:message-id:subject :from:to:content-type; bh=Tnc55xk+/PlAVQPTHmi7hLJK7/9jq9gDAmkSQa3EHDw=; b=vUmMn/nfLl6wg0zKJ1F0MKrRssrwlPgS+z+rV1i2SuEEZ7QFsG1JKrsHba/Qi7F3Dn pR1yYZxyH+jCR/aBNGeLGl6rrfypxy2xpVysUvkWkEx+fnMIEgr+FjFE7S8qWtq/Ik7r FDGbwZZvn0a75RwYiQ82AKTkvr3zu72Jr1/VM=*Domainkey-signature*: a=rsa-sha1; c=nofws; d=gmail.com; s=gamma; h=mime-version:date:message-id:subject:from:to:content-type; b=Y4YalCPXUPxmMUSbGh71zcjsKAzZ2LeXQfhRJ4WxnzxUQPzhnwluuTWGmqx/gudn3w xcs4eZ+AAXUPVWMeYz48+5WcS1ejXST+SSvaqz9VkLCHOa8Ri8qkA/gO5c4t4+yKUmVJ vE7peDosIgPjBoVBMkf1KkGgjMJp3uBkEHh90=

Hi,

I'm more and more convinced that our current design of our _expression_ template mechanism is still not very good and some improvements are required to make it more scalable to new matrix types. For instance, for sparse matrices I had to create clones of most of the _expression_ classes we have in the Core module. E.g., there are SparseCwiseBinaryOp, SparseCwise, SparseTranspose, etc. Even though, e.g., CwiseBinaryOp and SparseCwiseBinaryOp have only very little in common, this till represent a non negligeable bit of code duplication. Especially the Cwise proxy class are essentially the same.

Even though we would only correctly support dense and sparse matrices, there is still a major limitation: one cannot write the type of a generic _expression_ dealing with either dense matrices or sparse matrices. For instance see what I had to do for the AutoDiff module: instead of simply writing CwiseBinaryOp<ei_scalar_sum_op<Scalar>,A,B>, I introduced a MakeCwiseBinaryOp<> little struct in Core/ExpressionMaker.h returning the correct type of a cwise binary op accoring to the kind of matrices/expressions we are dealing with. Such and helper class has to be written for every _expression_ types and specialized for every kind of matrices.

Likewise one cannot write generic _expression_ analyser only because the name of the template class representing, e.g., a transpose operation, depends on the kind of matrices!

Of course things are getting wors as we want to support new kind of matrix storages. You might argue that matrices with special storage are only useful for solving, but you always need matrix products involving scalar multiples, transpose, conjutage, etc. So it seems to me that finaly all kind of matrices need a pretty much complete support for _expression_ templates.

Well, all in all, I'd like to investigate some solutions to make this whole mess a little less messy, and yes I think this is something very important to do for 3.0 beta1. Well, unless someone show me the current design is already perfect....

OK, so the general idea is to try to decouple the semantic of an _expression_ from its implementation. E.g.: the type of 2*M should always be CwiseUnaryOp<ei_scalar_multiple<Scalar>, typeof(M)> even though it is evaluated differently according to the storage of M.

To this end, I think we could introduce a "StorageType" integer to the matrix traits. We could also use the Flags, but adding a new attribute for that seems to be easier to use and more scalable.

Then, each _expression_ could inherit an implementation class where one of its template parameter would be the StorageType. This way one could add new storage type and only focuses on its implementation specificities. The implementation of all basics operators (all unary op, +, -, the entire .cwise() prefix, etc.) could be shared for all matrix types.

Of course I'm still unsure about many of the details and I probably overseen a lot of difficulties, but it seems to be a saner design for the future. Discussions are open ;)

Cheers,

Gael.

**Follow-Ups**:**Re: [eigen] General design issue***From:*Benoit Jacob

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