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One of the fun things you can do with Cartesian is easily generate unrolled matrix multiplication algorithms. This might be particularly useful for a fixed-size array type (see also ImmutableArrays; t…
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# Please follow the general troubleshooting steps first:
- [x] Ran `brew update` and retried your prior step?
- [x] Ran `brew doctor`, fixed as many issues as possible and retried your prior step?
###…
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I would like to suggest that matrix types with structural zeros make this structure evident in the default printing. For example, the lower triangulat elements of UpperTriangular instances are guarant…
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`factorization.jl` is currently undergoing some maintenance (see #3089), but here is another problem that has not been mentioned there. For convenience when reading this, recall that:
```
typealias S…
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Dealing with polynomial rings is nice, but the fact that they always present their content in fully expanded form can make it hard to read results. For a single polynomial, computing its factorizati…
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Following [this](https://groups.google.com/forum/#!topic/julia-users/fdFqAffyxIY) issue about the reuse of CHOLMOD.Factor:
``` julia
julia> F = cholfact(speye(5))
```
returns a `Base.SparseMatrix.CH…
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I've written a pure-julia implementation of a modified cholesky factorization (GMW81), which can be used in Newton-like methods to guarantee descent. (It's worth noting that [this line](https://github…
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Update: There are [multiple problems](https://github.com/JuliaLang/julia/issues/14005#issuecomment-156849772) that cause the original issue. Use this issue to track the most directly related linalg is…
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The benchmark in Fig. 1 of https://web.stanford.edu/~boyd/papers/pdf/ecos_ecc.pdf appears to show ECOS performing substantially better than Elemental's current distributed solver run with a single MPI…
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For dense matrices of size n, m with n >= m, it seems like
the function rank could be made faster by computing the cholesky decomposition
``` julia
m = 10
for n in (100, 1000, 10000, 100000)
A = …