Comments (3)
What you say makes sense but would require breaking so much code.
alternatively, you can define your own CSC type, and the functions in this package should work on it.
from sparsearrays.jl.
Halving memory requirements is not a scalable solution, unfortunately. Once your problems are twice as big, you run into the same problem.
from sparsearrays.jl.
This is done pretty commonly, especially in distributed settings. But we can't support it here.
from sparsearrays.jl.
Related Issues (20)
- strange method definition overwritten WARNING in tests HOT 3
- Solving a system of linear equations `A\b` uses up more memory when `A` is sparse then when `A` is dense HOT 1
- typos HOT 1
- Document what `issparse(A)` guarantees or requires
- Displaying large sparse matrices in REPL takes too long HOT 7
- SparseArrays: + has type-instability for `ArbNumerics.jl` type `ArbFloat` HOT 3
- `vcat`-related method ambiguities HOT 4
- SparseArrays.nzrange could benefit from a Base.@propagate_inbounds
- Unable to sum `::SparseVector{Num,Int}`s HOT 1
- Mishandling of lu on view of SparseMatrixCSC HOT 3
- Adding views of sparse matrices generates a dense matrix
- GPU component of CHOLMOD as package extension HOT 3
- JLL-style LibSuiteSparse headers are incompatible with new platforms HOT 3
- Document usage of UMFPACK control vector
- Allow UMFPACK to be run with an alternative BLAS under the hood HOT 13
- SparseMatrixCSC type conversion (sometimes) drops structural nonzeros
- Factorize a sparse symmetric indefinite matrix
- Regression for `mul!` from 1.9 to 1.10 HOT 13
- Using `@view` leads to 100x performance loss HOT 7
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from sparsearrays.jl.