for this very nasty bug (unaligned member in dynamically allocated struct)
that our friends at Krita just encountered:
http://bugs.kde.org/show_bug.cgi?id=177133
CCBUG:177133
- in matrix-matrix product, static assert on the two scalar types to be the same.
- Similarly in CwiseBinaryOp. POTENTIALLY CONTROVERSIAL: we don't allow anymore binary
ops to take two different scalar types. The functors that we defined take two args
of the same type anyway; also we still allow the return type to be different.
Again the reason is that different scalar types are incompatible with vectorization.
Better have the user realize explicitly what mixing different numeric types costs him
in terms of performance.
See comment in CwiseBinaryOp constructor.
- This allowed to fix a little mistake in test/regression.cpp, mixing float and double
- Remove redundant semicolon (;) after static asserts
* add a LDL^T factorization with solver using code from T. Davis's LDL
library (LPGL2.1+)
* various bug fixes in trianfular solver, matrix product, etc.
* improve cmake files for the supported libraries
* split the sparse unit test
* etc.
Some naming questions:
- for "extend" we could also think of: "expand", "union", "add"
- same for "clamp": "crop", "intersect"
- same for "contains": "isInside", "intersect"
=> ah "intersect" is conflicting, so that eliminates this one !
* remove the automatic resizing feature of operator =
* add function Matrix::set() to be used when the previous
behavior is wanted
* the default constructor of dynamic-size matrices now
creates a "null" matrix (data=0, rows = cols = 0)
instead of a 1x1 matrix
* fix UnixX typos ;)
as described on the wiki (one map per N column)
Here's some bench results for the 4 currently supported map impl:
std::map => 18.3385 (581 MB)
gnu::hash_map => 6.52574 (555 MB)
google::dense => 2.87982 (315 MB)
google::sparse => 15.7441 (165 MB)
This is the time is second (and memory consumption) to insert/lookup
10 million of coeffs with random coords inside a 10000^2 matrix,
with one map per packet of 64 columns => google::dense really rocks !
Note I use for the key value the index of the column in the packet (between 0 and 63)
times the number of rows and I used the default hash function.... so maybe there is
room for improvement here....
