Comments (2)
Hm, we should probably to some type promotion then. Is typejoin
the correct function?
from blockarrays.jl.
No I don't think so. In the case of N = 1
this could be done by comprehension:
[arr[globalrange(block_sizes, K)] for K=1:nblocks(block_sizes,1)]
should automatically infer the correct type (SparseVector
if arr
is a SparseVector
, otherwise normally Vector
).
I'm not sure how to do comprehension via generated functions though.
from blockarrays.jl.
Related Issues (20)
- Deprecate blockcolsupport(A, ::Integer) HOT 1
- Structure of blocks matrix lost on broadcast or `similar`
- Replace PseudoBlockArray with view?
- Drop support for mixed blocks in broadcast?
- ldiv for PseudoBlockMatrix two times slower than for native Matrix HOT 2
- Incompatible with LinearSolve.jl HOT 4
- Ambiguity error on Julia nightly HOT 3
- Handle empty blocks upon `motar` HOT 3
- v[Block(1,2)] should error
- Code running on local Julia, but failing on CI tests with the last version of BlockArrays HOT 2
- Support multi-threading
- Matrix multiplication error when using BlockArrays and CUDA HOT 9
- Very slow matrix-vector multiplication HOT 5
- Regression in broadcasting between blockarray and normal array HOT 2
- Should we special-case block diagonal matrices? HOT 3
- missing `similar` method for `PseudoBlockArray` (`mapslices` unusable) HOT 4
- Ambiguity causing broadcast overload HOT 1
- Block indexing are not copies HOT 1
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- Tag v1.0
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from blockarrays.jl.