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Introduce a common type for differentiated retractions as vector transport and two such transports for Stiefel #318
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Codecov Report
@@ Coverage Diff @@
## master #318 +/- ##
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+ Coverage 95.97% 95.99% +0.01%
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Files 69 69
Lines 4646 4667 +21
==========================================
+ Hits 4459 4480 +21
Misses 187 187
Continue to review full report at Codecov.
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…default retraction (default inverse retraction.
Co-authored-by: Mateusz Baran <[email protected]>
Co-authored-by: Mateusz Baran <[email protected]>
…iaManifolds/Manifolds.jl into kellertuer/differentiated-retraction
The failure on Julia nightly is now caused by this: JuliaPy/PyCall.jl#873 . |
Thanks for investigating, I did not yet have the time to investigate (and was happy to get pyplot-stuff running in the first place, which took some time and tries). |
No problem, I think it's good to see why nightly builds are failing once in a while. BTW, I just noticed that the description of scaled transport has some issues: https://juliamanifolds.github.io/Manifolds.jl/previews/PR318/interface.html#ManifoldsBase.ScaledVectorTransport . |
Thanks for noticing there is a space missing; I will push that fix to master and it will then get into the next version, |
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OK, I don't have any other comments here 🙂 .
Cool! I just bumped the version and will then merge in a few minutes. |
Here's my little Christmas project. 🎄
I read a little bit about two new differentiated retractions (they are actually available in ROPTLIB (https://github.com/whuang08/ROPTLIB), but not that well documented.
d*d'
should be 1000x100 forStiefel(1000,2)
which should be avoided by changing order of compuations.