Manifolds.jl
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JuliaManifolds
Manifolds.jl provides a library of manifolds aiming for an easy-to-use and fast implementation.
The approach for approximating the logarithm map of GL(n) and SL(n) in #249 is to project the point to the nearest point for which we know how to exactly compute...
TODO: check if https://github.com/JuliaDiff/AbstractDifferentiation.jl can be used for specifying differentiation backend (also relevant to Manopt.jl).
These are the points still remaining after PR #319 : - [ ] Can we do a test for https://codecov.io/gh/JuliaManifolds/Manifolds.jl/src/master/src/groups/metric.jl#L216 - [ ] How can we best test the `Powerdistribution`?...
It's probably worth thinking about some of the most common plots users might want to make related to manifolds and see if we can identify a small subset of utility...
After recently reading a bit about use of cotangent vectors in AD (for example here: https://gist.github.com/Keno/4a6507b75288b1fe671e9d1cc683014f ) I think we handle them wrong. Now I think there are only two...
It would be interesting to have a type for manifolds defined by an equation `F(x)=0` with the metric inherited from the embedding.
I checked how hard would it be to add GPU support to Manifolds.jl. Essentially without some kind of contextual dispatch (like this: https://github.com/JuliaGPU/CUDAnative.jl/pull/334 ) it would require rewriting almost everything.
As discussed here: https://github.com/JuliaNLSolvers/Manifolds.jl/pull/71#issuecomment-569412815
Hello, following this [discussion](https://discourse.julialang.org/t/posdefmanifoldml-machine-learning-for-positive-definite-matrices/32620/15), the steps followed by [PosDefManifold.jl](https://github.com/Marco-Congedo/PosDefManifoldML.jl) for doing classification in the tangent space are: 1) parallel transport of all points to the center of the manifold (identity...
As a continuation of #49 the two papers * [ ] https://arxiv.org/pdf/1209.0068.pdf * [x] https://arxiv.org/abs/1604.05054 see [this inverse redaction](https://juliamanifolds.github.io/Manifolds.jl/stable/manifolds/stiefel.html#ManifoldsBase.inverse_retract-Tuple{MetricManifold{ℝ,%20%3C:Stiefel{%3C:Any,%20ℝ},%20CanonicalMetric},%20Any,%20Any,%20ApproximateLogarithmicMap}) provide further methods on the Stiefel and fixed rank manifold, though...
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Manifolds.jl provides a library of manifolds aiming for an easy-to-use and fast implementation.