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use diagonaltensormap #190

Merged
merged 2 commits into from
Dec 19, 2024
Merged

use diagonaltensormap #190

merged 2 commits into from
Dec 19, 2024

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Jutho
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@Jutho Jutho commented Dec 17, 2024

This starts using the DiagonalTensorMap type in svd and eigenvalue decompositions. It also fixes #181 .

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codecov bot commented Dec 17, 2024

Codecov Report

Attention: Patch coverage is 89.62264% with 11 lines in your changes missing coverage. Please review.

Project coverage is 82.22%. Comparing base (c041bfe) to head (efe4ce0).
Report is 2 commits behind head on master.

Files with missing lines Patch % Lines
src/tensors/factorizations.jl 89.62% 11 Missing ⚠️
Additional details and impacted files
@@            Coverage Diff             @@
##           master     #190      +/-   ##
==========================================
- Coverage   83.16%   82.22%   -0.94%     
==========================================
  Files          42       42              
  Lines        5257     5300      +43     
==========================================
- Hits         4372     4358      -14     
- Misses        885      942      +57     

☔ View full report in Codecov by Sentry.
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I dug a bit into the LinearAlgebra code, for the scalartype they use: https://github.com/JuliaLang/LinearAlgebra.jl/blob/1137b4c7fa8297cef17c4ae0982d7d89d4ab7dd8/src/eigen.jl#L319
I don't know if it makes a difference what the actual implementation is, but I quite like if there is a single function that could be overloaded if needed to control this behavior.

Otherwise looks good to me!

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Jutho commented Dec 18, 2024

@lkdvos , is the latest commit more in line but what you had in mind?

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lkdvos commented Dec 19, 2024

Yes, exactly! I also like that you factored out the "copy of type" function, looks very clean to me!

@Jutho Jutho merged commit a8aa774 into master Dec 19, 2024
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convert to floating point in matrix factorisations
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