On 15 Aug 2009, at 18:25, joel wrote:
Edward Grace wrote:
Well, semantics I know but they are all tensors. A
scalar -> tensor of order 0, vector -> a tensor of order 1, matrix -> a tensor of order 2. ;-) Ok back to this. I had a discussionw ith my co-worker on this matter and our current code state. Well, in fact, in MATLAB, the concept of LinAlg matrix and of multi-diemnsional container are merged.
Indeed.
So now, what about the following class set :
- table : matlab like "matrix" ie container of data that can be N-Dimensions. Only suppot elementwise operations.
Ok. Sounds good.
- vector/covector : reuse table component for memory management + add lin. alg. vector semantic. Can be downcasted to table and table can be explciitly turned into vector/covector baring size macthing.
Yes. By 'table -> vector/covector' do you mean in a similar manner to 'reshape' in MATLAB? If so good.
- matrix : ditto 2D lin. alg. object, interact with vector/covector as it should. Supprot algebra algorithm. Can have shape etc ... - tensor : ditto with tensor.
Yes, I think that makes a lot of sense.
Duck typing + CRTP + lightweight CT inheritance make all these interoperable.
Duck typing...... (frantically searches the web)... Hah hah ha!!! I'd never come across that phrase before, very funny. "Quack Quack" or should that be "Coin Coin!"?
Leads to nice stuff like :
vector * vector = outer product
By 'outer product' in the above on the RHS do you mean a matrix or a specific object of type 'outer product' which is aware of its structure? As I mentioned before the outer product thus formed will not have N^2 independent components if the two vectors are of length N. Consequently it could be considered a 'special' type of matrix. As a concrete example, consider the following, operator (x) is outer product, . inner (dot) product, I the identity matrix. this forms a projection operator that, when applied to a vector v (from the left), will project it on to the orthogonal subspace of k - (well I think so, I may have made a boo-boo)! As you can see from the structure of the outer product the number of independent components in P_k is actually a lot smaller than the total size of the formed matrix. However, from a typing point of view, what's the type of Pk - it's clearly not of type 'outer_product'? So, could we write something like: P = 2*(identity(4) - outer_product(k,k)/inner_product(k,k)); so that P ends up being aware of its lack of independent components and acts accordingly? When dealing with small vector spaces (e.g. R^4) this wouldn't matter of course - forming a full dense matrix is probably best however if you are in, say, R^1000 why bother with all the redundant repetition? [that's a joke b.t.w. 'repetition' is redundant]
covector * vector = scalar product
Yep.
vector* covector = matrix
Yep.
matrix*vector and covector*matrix are properly optimized etc...
What do you htink of this then ?
Sounds good to me. When's it finished? ;-) -ed ------------------------------------------------ "No more boom and bust." -- Dr. J. G. Brown, 1997
