Matrix `exp` and `log` are not inverse of each other #54833

olivierverdier · 2024-06-17T15:54:40Z

Output of versioninfo()

Julia Version 1.10.3
Commit 0b4590a (2024-04-30 10:59 UTC)
Build Info:
Official https://julialang.org/ release
Platform Info:
OS: macOS (arm64-apple-darwin22.4.0)
CPU: 8 × Apple M1
WORD_SIZE: 64
LIBM: libopenlibm
LLVM: libLLVM-15.0.7 (ORCJIT, apple-m1)
Threads: 1 default, 0 interactive, 1 GC (on 4 virtual cores)
Environment:
JULIA_EDITOR = emacsclient
JULIA_INPUT_COLOR = bold
JULIA_ANSWER_COLOR = bold

How Julia was installed

With juliaup

Minimal working example:

M = [0.9949357359852791 -0.015567763143324862 -0.09091193493947397 -0.03994428739762443 0.07338356301650806; 0.011813655598647289 0.9968988574699793 -0.06204555000202496 0.04694097614450692 0.09028834462782365; 0.092737943594701 0.059546719185135925 0.9935850721633324 0.025348893985651405 -0.018530261590167685; 0.0369187299165628 -0.04903571106913449 -0.025962938675946543 0.9977767446862031 0.12901494726320517; 0.0 0.0 0.0 0.0 1.0]
sdiff = exp(log(M)) - M
-log10(maximum(abs.(sdiff))) # < 3

The same test in Python gives

import numpy as np
import scipy as sp


M = np.array([[0.9949357359852791,-0.015567763143324862,-0.09091193493947397,-0.03994428739762443,0.07338356301650806],[0.011813655598647289,0.9968988574699793,-0.06204555000202496,0.04694097614450692,0.09028834462782365],[0.092737943594701,0.059546719185135925,0.9935850721633324,0.025348893985651405,-0.018530261590167685],[0.0369187299165628,-0.04903571106913449,-0.025962938675946543,0.9977767446862031,0.12901494726320517],[0.0,0.0,0.0,0.0,1.0]])

sdiff = sp.linalg.expm(sp.linalg.logm(M)) - M

print(-np.log10(np.max(np.abs(sdiff)))) # > 15

The text was updated successfully, but these errors were encountered:

dkarrasch · 2024-06-17T16:10:48Z

Could you please quickly check which one seems to be wrong: log or exp?

oscardssmith · 2024-06-17T16:15:21Z

reproduced on x86 linux. The issue appears to be in log (although it's somewhat hard to prove because matrix logs are not unique).

olivierverdier · 2024-06-17T20:10:59Z

A simpler example:

N = [0.0 -0.8 1.1 0.2 -0.2; 0.8 0.0 0.8 -0.4 -0.1; -1.1 -0.8 0.0 0.4 -0.1; -0.2 0.4 -0.4 0.0 0.0; 0.0 0.0 0.0 0.0 0.0]

k = 10 # also bad for k = 100 or 1000

sdiff = log(exp(N/k)) - N/k
@show -log10(maximum(abs.(sdiff))) # 2.9

I agree with @oscardssmith , I suspect the matrix logarithm to be at fault.

Edit: for completeness, the Python code

N = np.array([[0.0, -0.8, 1.1, 0.2, -0.2], [0.8, 0.0, 0.8, -0.4, -0.1], [-1.1, -0.8, 0.0, 0.4, -0.1], [-0.2, 0.4, -0.4, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0, 0.0],])

k = 10

sdiff = sp.linalg.logm(sp.linalg.expm(N/k)) - N/k
print(-np.log10(np.max(np.abs(sdiff)))) # 15.0

oscardssmith · 2024-06-17T20:24:09Z

This second example isn't as good because iiuc, log(exp(M)) == M isn't expected for matrices do to branch cuts.

dkarrasch · 2024-06-17T20:35:40Z

On v1.6.7 I get

julia> -log10(maximum(abs.(sdiff))) # < 3
15.175451669208671

on v1.9.4 and v1.8.5

julia> -log10(maximum(abs.(sdiff))) # < 3
2.233375639545474

so this must be an "old" bug.

olivierverdier · 2024-06-17T20:44:34Z

This second example isn't as good because iiuc, log(exp(M)) == M isn't expected for matrices do to branch cuts.

That's why I included the parameter k. If M is small enough, I would expect log(exp(M)) == M to be true no matter what, right?

olivierverdier · 2024-06-17T21:15:57Z

so this must be an "old" bug.

I suspect the problem to be in log_quasitriu, which was introduced in 6913f9c, in 2021.

giordano · 2024-06-17T21:31:09Z

I suspect the problem to be in log_quasitriu, which was introduced in 6913f9c, in 2021.

That's more recent than Julia v1.6 though

olivierverdier · 2024-06-17T21:33:58Z

More evidence that the problem is the logarithm:

N = [0.0 -0.8 1.1 0.2 -0.2; 0.8 0.0 0.8 -0.4 -0.1; -1.1 -0.8 0.0 0.4 -0.1; -0.2 0.4 -0.4 0.0 0.0; 0.0 0.0 0.0 0.0 0.0]
k = 10
M = exp(N/k)

sdiff = log(M*M) - 2*log(M)

@show -log10(maximum(abs.(sdiff))) # 2.6

The equivalent code in Python gives 14.7.

olivierverdier · 2024-06-17T21:45:44Z

I suspect the problem to be in log_quasitriu, which was introduced in 6913f9c, in 2021.

That's more recent than Julia v1.6 though

If it were the case that PR #39973 is to blame (wild guess), then the bug would have appeared first in v1.7.0.

olivierverdier · 2024-06-18T08:15:48Z

For completeness, I checked the code above (i.e., with log(M*M) - 2*log(M)) with various versions, and indeed, the bug appears between v1.6.7 and v1.7.3.
My money is still on PR #39973.

olivierverdier · 2024-06-18T16:44:17Z

An even simpler test:

function test_log(N, k=10)
    M = exp(N / k)
    sdiff = log(M*M) - 2*log(M)
    return -log10(maximum(abs.(sdiff)))
end

Now simply with

N = [0.0 -1.0 1.0; 1.0 0.0 0.0; 0.0 0.0 0.0]

run

test_log(N) # 2.1

and the same in Python returns 15.4.

dkarrasch · 2024-06-20T12:58:24Z

It's not clear to me what is the cause. I tried a specific test from scipy's test suite, and this is the result:

julia> VERSION
v"1.10.4"

julia> A = [3.2346e-01 3.0000e+04 3.0000e+04 3.0000e+04;
              0.0000e+00 3.0089e-01 3.0000e+04 3.0000e+04;
              0.0000e+00 0.0000e+00 3.2210e-01 3.0000e+04;
              0.0000e+00 0.0000e+00 0.0000e+00 3.0744e-01];

julia> logA = [ -1.12867982029050462e+00  9.61418377142025565e+04 -4.52485573953179264e+09  2.92496941103871812e+14;
         0.00000000000000000e+00 -1.20101052953082288e+00 9.63469687211303099e+04 -4.68104828911105442e+09;
         0.00000000000000000e+00  0.00000000000000000e+00 -1.13289322264498393e+00  9.53249183094775653e+04;
         0.00000000000000000e+00  0.00000000000000000e+00 0.00000000000000000e+00 -1.17947533272554850e+00];

julia> (logA - log(A)) / norm(logA)
4×4 Matrix{Float64}:
 0.0  5.97008e-25  9.78138e-21  -1.06839e-15
 0.0  0.0          4.97507e-25   1.30418e-20
 0.0  0.0          0.0           2.98504e-25
 0.0  0.0          0.0           0.0

julia> (exp(logA) - A) / norm(A)
4×4 Matrix{Float64}:
 -1.81534e-8  0.000297088  50.5783       -2.79971e6
  0.0         2.33977e-8    0.00116877    8.26782
  0.0         0.0           3.53834e-10  -0.00160443
  0.0         0.0           0.0          -3.34387e-8

julia> (exp(log(A)) - A) / norm(A)
4×4 Matrix{Float64}:
 -1.81534e-8  0.000297088  50.5783       -2.79971e6
  0.0         2.33977e-8    0.00116877    8.26782
  0.0         0.0           3.53834e-10  -0.00160443
  0.0         0.0           0.0          -3.34387e-8

julia> (exp(log(A)) - A)
4×4 Matrix{Float64}:
 -0.001334  21.8314       3.71673e6     -2.05736e11
  0.0        0.00171937  85.8868         6.07558e5
  0.0        0.0          2.60014e-5  -117.901
  0.0        0.0          0.0           -0.00245723

OTOH, we have:

>>> (sp.linalg.expm(A_logm) - A) / sp.linalg.norm(A)
array([[ 0.00000000e+00,  1.33667877e-15,  3.49612787e-10,
        -5.04994630e-07],
       [ 0.00000000e+00,  0.00000000e+00,  1.23766552e-15,
         4.78558722e-11],
       [ 0.00000000e+00,  0.00000000e+00,  0.00000000e+00,
         1.38618539e-15],
       [ 0.00000000e+00,  0.00000000e+00,  0.00000000e+00,
         0.00000000e+00]])

So, the computed log is really close to the "known" solution, but the exp of it is completely wrong in the upper right corner.

ADDENDUM: It seems, however, that this specific example fails on all versions starting from 1.6.x.

mikmoore · 2024-06-20T14:12:01Z

I tried a specific test from scipy's test suite

It's worth noting that the condition number of that matrix is awful:

julia> cond(A)
1.8884133342622014e20

julia> cond(logA)
4.086839610043594e28

I have an independent myexp implementation (our actual exp implementation is better but has limited type support #51008) and it similarly struggles with this:

function myexp(A)
	# compute exp(A) == exp(A/2^n)^(2^n)
	iszero(A) && return float(one(A))
	# TODO: decide the best norm to use here: op-1, op-Inf, or frobenius are probably good candidates - maybe take the min of all three
	nA = float(norm(A,2))
	fr,ex = frexp(nA)
	logscale = max(4 + ex,0) # ensure nA^(4+1)/factorial(4+1) < eps for 4 or whatever power our approximation is
	p = ntuple(i->inv(prod(1.0:i-1)),Val(9))
	scale = exp2(-logscale)
	sA = A * scale
	if sA isa AbstractMatrix
		expA = evalpoly(sA,UniformScaling.(p))
	else
		expA = evalpoly(sA,p)
	end
	for _ in 1:logscale
		expA = expA*expA
	end
	return expA
end

And with this and BigFloat:

julia> norm(exp(logA) - A) / norm(A)
2.799714043033207e6

julia> norm(myexp(logA) - A) / norm(A) # myexp is generally worse
5.134964610902018e6

julia> norm(myexp(big.(logA)) - A) / norm(A) |> Float64 # despite being worse, BigFloat makes this version work
8.141481015780612e-7

Note that the squaring part of our exp routine is probably very-much to blame, given that BigFloat resolves that problem even though the exp kernel is not expanded beyond what is roughly needed for Float64. I don't think we should use that example for this particular issue.

However, myexp does nothing to resolve the original example:

julia> cond(M) # extremely well conditioned
1.1906431494194285

julia> norm(exp(log(M)) - M) / norm(M) |> Float64
0.003052024023511132

julia> norm(myexp(big.(log(M))) - M) / norm(M) |> Float64
0.0030520240235111305

Given that the matrix exponential is reasonably "simple" to compute (modulo stability issues, which even the literature has not fully settled), I am much more inclined towards expecting that log is the culprit.

mikmoore · 2024-06-20T14:30:54Z

Confirmation that log is the culprit:

function eigapply(fun, A)
	D,V = eigen(A)
	return V * Diagonal(fun.(D)) / V
end

julia> norm(exp(log(M)) - M) / norm(M)
0.003052024023511132

julia> norm(exp(eigapply(log,M)) - M) / norm(M)
6.499342713344915e-16

olivierverdier · 2024-06-20T14:56:30Z

I think the problem is in log_quasitriu. From the example above (i.e., qtriu = schur(exp(N/10)).T):

qtriu = [0.9950041652780259 -0.09983341664682815 0.07153667745275474; 0.09983341664682804 0.9950041652780259 0.06981527929449967; 0.0 0.0 1.0]

Now

LinearAlgebra.log_quasitriu(qtriu)[:,end]

gives

0.07240564253650691
 0.06891743457599916
 0.0

and in Python, the same vector is

0.07496781758158656
 0.06618025632357401
 0.0

Pretty big discrepancy starting at the second decimal.

oscardssmith · 2024-06-20T19:04:23Z

So this code is a very direct matlab translation with all the oddness that entails, but what I've found is that if we make it so s,m = 1,4 rather than 0,5 we get the right answer. see #54867

aravindh-krishnamoorthy · 2024-08-30T11:33:45Z

Just one more pointer to add here for further analysis. Kinda good that the differences are localised.

julia> using LinearAlgebra

julia> M = [0.9949357359852791 -0.015567763143324862 -0.09091193493947397 -0.03994428739762443 0.07338356301650806; 0.011813655598647289 0.9968988574699793 -0.06204555000202496 0.04694097614450692 0.09028834462782365; 0.092737943594701 0.059546719185135925 0.9935850721633324 0.025348893985651405 -0.018530261590167685; 0.0369187299165628 -0.04903571106913449 -0.025962938675946543 0.9977767446862031 0.12901494726320517; 0.0 0.0 0.0 0.0 1.0]
5×5 Matrix{Float64}:
 0.994936   -0.0155678  -0.0909119  -0.0399443   0.0733836
 0.0118137   0.996899   -0.0620456   0.046941    0.0902883
 0.0927379   0.0595467   0.993585    0.0253489  -0.0185303
 0.0369187  -0.0490357  -0.0259629   0.997777    0.129015
 0.0         0.0         0.0         0.0         1.0

julia> S = schur(complex(M));

julia> norm(exp(S.Z*LinearAlgebra.log_quasitriu(S.T)*S.Z') - M)
1.5337045591657124e-15

julia> S = schur(M);

julia> norm(exp(S.Z*LinearAlgebra.log_quasitriu(S.T)*S.Z') - M)
0.0068453336198366545

julia> exp(S.Z*LinearAlgebra.log_quasitriu(S.T)*S.Z') - M
5×5 Matrix{Float64}:
 -6.66134e-16  -3.72966e-16   2.498e-16    -1.38778e-17  -0.00185147
 -3.34802e-16   5.55112e-16  -2.15106e-16   4.85723e-17   0.00300777
 -6.93889e-17  -2.70617e-16  -2.22045e-16  -2.08167e-17   0.00584284
 -1.249e-16    -6.245e-17    -7.63278e-17   0.0          -0.000495109
  0.0           0.0           0.0           0.0           0.0

Eigenvectors and eigenvalues of M and exp(log(M)):

julia> using LinearAlgebra

julia> M = [0.9949357359852791 -0.015567763143324862 -0.09091193493947397 -0.03994428739762443 0.07338356301650806; 0.011813655598647289 0.9968988574699793 -0.06204555000202496 0.04694097614450692 0.09028834462782365; 0.092737943594701 0.059546719185135925 0.9935850721633324 0.025348893985651405 -0.018530261590167685; 0.0369187299165628 -0.04903571106913449 -0.025962938675946543 0.9977767446862031 0.12901494726320517; 0.0 0.0 0.0 0.0 1.0]
5×5 Matrix{Float64}:
 0.994936   -0.0155678  -0.0909119  -0.0399443   0.0733836
 0.0118137   0.996899   -0.0620456   0.046941    0.0902883
 0.0927379   0.0595467   0.993585    0.0253489  -0.0185303
 0.0369187  -0.0490357  -0.0259629   0.997777    0.129015
 0.0         0.0         0.0         0.0         1.0

julia> E1 = eigen(M);

julia> E2 = eigen(exp(log(M)));

julia> norm(E1.values - E2.values)
1.2787159664228656e-15

julia> norm(E1.vectors - E2.vectors)
0.028242416081603217

julia> abs.(E1.vectors - E2.vectors)
5×5 Matrix{Float64}:
 3.33356e-16  3.33356e-16  4.54082e-15  4.54082e-15  0.0120785
 1.34378e-15  1.34378e-15  4.87741e-15  4.87741e-15  0.0141492
 6.66134e-16  6.66134e-16  4.26807e-15  4.26807e-15  0.00359654
 3.34869e-16  3.34869e-16  0.0          0.0          0.0209428
 0.0          0.0          0.0          0.0          9.55047e-5

The likely "correct" answer based on Schur-Parlett recurrence algorithm (unique roots required):

julia> S = schur(M);

julia> norm(exp(S.Z*real(MatrixFunctions.fm_schur_parlett_recurrence(log, S.T))*S.Z') - M)
1.1184124283538868e-15

julia> norm(exp(S.Z*LinearAlgebra.log_quasitriu(S.T)*S.Z') - M)
0.0068453336198366545

julia> real(MatrixFunctions.fm_schur_parlett_recurrence(log, S.T))
5×5 Matrix{Float64}:
 -1.09981e-15   0.117707      4.04932e-15  2.60264e-15   0.141422
 -0.117707     -1.09981e-15   1.77951e-15  3.03452e-16   0.0482574
  0.0           0.0           2.99457e-16  0.0544547    -0.0213389
  0.0           0.0          -0.0544547    2.99457e-16   0.0881419
  0.0           0.0           0.0          0.0           0.0

julia> LinearAlgebra.log_quasitriu(S.T)
5×5 Matrix{Float64}:
 -1.10068e-15   0.117707      4.06711e-15  2.69458e-15   0.143336
 -0.117707     -1.10068e-15   1.59252e-15  2.22818e-16   0.0419473
  0.0           0.0           2.9989e-16   0.0544547    -0.0195324
  0.0           0.0          -0.0544547    2.9989e-16    0.0885489
  0.0           0.0           0.0          0.0           0.0

aravindh-krishnamoorthy · 2024-08-30T14:50:08Z

Now, looking a bit more into the code, I'm confused.

The function _log_quasitriu!

julia/stdlib/LinearAlgebra/src/triangular.jl

Line 1947 in b6d2155

function _log_quasitriu!(A0, A)

utilizes

julia/stdlib/LinearAlgebra/src/triangular.jl

Line 1949 in b6d2155

m, s = _find_params_log_quasitriu!(A)

to compute the Pade approximation parameters m and s. However, the function

julia/stdlib/LinearAlgebra/src/triangular.jl

Line 1998 in b6d2155

function _find_params_log_quasitriu!(A)

is based on [R1], which is only for triangular matrices and not quasi-triangular matrices. Nevertheless, it is applied to both triangular and quasi-triangular matrices. Furthermore, the suggested algorithm [16, Alg. 6.3] in [R1] for T^(1/2) is replaced with _sqrt_quasitriu! in [R2] (according to the function header).

Question:

Has anyone studied whether the algorithm in [R1] works with quasi-triangular matrices?
- To me, the use seems odd since the algorithm in [R1] considers only the diagonal elements of a quasi-triangular matrix to compute s.

Note: I've taken note of @oscardssmith's fix where using s=1, m=5 results in a good solution instead of s=0, m=6 obtained from _find_params_log_quasitriu!. However, I'm not sure if forcing a square root in every case is the right approach.

[R1]: Al-Mohy, Awad H. and Higham, Nicholas J. "Improved Inverse Scaling and Squaring
Algorithms for the Matrix Logarithm", 2011, url: https://eprints.maths.manchester.ac.uk/1687/1/paper11.pdf
[R2]: Deadman, Edvin and Higham, Nicholas J. and Ralha, Rui, "Blocked Schur Algorithms for Computing the
Matrix Square Root," 2012, url: https://eprints.maths.manchester.ac.uk/1926/1/EdvinDeadmanPARA2012.pdf

aravindh-krishnamoorthy · 2024-09-03T16:14:10Z

Has anyone studied whether the algorithm in [R1] works with quasi-triangular matrices?

This does not seem to be the immediate problem. However, using the diagonal elements in the following seems wrong.

julia/stdlib/LinearAlgebra/src/triangular.jl

Line 2030 in 34b81fb

d = complex.(@view(A[diagind(A)]))

Choosing d as the eigenvalues of A seems to be a better choice for quasi-triangular matrices. The rest of the algorithm seems Ok for quasi-triangular matrices.

Note: Nevertheless, for the given M in this issue, using eigenvalues instead of diagonal values does not cause a change in the computed parameters m = 6 and s = 0. This will need to be taken up separately.

Next stop: double-checking the Pade approximation for m=6, s=0 as computed by the current implementation of _find_params_log_quasitriu!.

aravindh-krishnamoorthy · 2024-09-03T16:49:25Z

The issue is in the function

julia/stdlib/LinearAlgebra/src/triangular.jl

Line 2169 in 34b81fb

Base.@propagate_inbounds function _sqrt_pow_diag_block_2x2!(A, A0, s)

where the case $s = 0$ is not handled. In fact, the function faithfully implements Algorithm 5.1 in [R1]. However, unfortunately, Algorithm 5.1 in [R1] seems incomplete and only applicable for $s \geq 1.$

The following is a quick fix that works for this case (changes are made to the code in the above commit). I will have to think a bit more before suggesting a permanent solution.

Base.@propagate_inbounds function _sqrt_pow_diag_block_2x2!(A, A0, s)
+    if iszero(s)
+        A[1,1] -= 1
+        A[2,2] -= 1
+        return
+    end
    _sqrt_real_2x2!(A, A0)
    if isone(s)
        A[1,1] -= 1
        A[2,2] -= 1
    else

With this change, we have

julia> norm(exp(S.Z*log_quasitriu(S.T)*S.Z') - M)
1.1192892392171394e-15

Interpolation leads to the computation of `exp(log(v))` which is broken in Julia (JuliaLang/julia#54833)

aravindh-krishnamoorthy · 2024-09-12T12:23:47Z

This code looks good also for long term. @oscardssmith would you like to update your PR's title/code and provide this fix instead? To avoid duplication of effort and the fact that you got started with this...

Update 24-Oct-2024: I will create a PR with this fix.

This PR is a potential fix for #54833. ## Description The function https://github.com/JuliaLang/julia/blob/2a06376c18afd7ec875335070743dcebcd85dee7/stdlib/LinearAlgebra/src/triangular.jl#L2220 computes $\boldsymbol{A}^{\dfrac{1}{2^s}} - \boldsymbol{I}$ for a real-valued $2\times 2$ matrix $\boldsymbol{A}$ using Algorithm 5.1 in [R1]. However, the algorithm in [R1] as well as the above function do not handle the case $s=0.$ This fix extends the function to compute $\boldsymbol{A}^{\dfrac{1}{2^s}} - \boldsymbol{I} \Bigg|_{s=0} = \boldsymbol{A} - \boldsymbol{I}.$ ## Checklist - [X] Fix code: `stdlib\LinearAlgebra\src\triangular.jl` in function `_sqrt_pow_diag_block_2x2!(A, A0, s)`. - [X] Add test case: `stdlib\LinearAlgebra\test\triangular.jl`. - [X] Update `NEWS.md`. - [X] Testing and self review. | Tag | Reference | | --- | --- | | <nobr>[R1]</nobr> | Al-Mohy, Awad H. and Higham, Nicholas J. "Improved Inverse Scaling and Squaring Algorithms for the Matrix Logarithm", 2011, url: https://eprints.maths.manchester.ac.uk/1687/1/paper11.pdf | --------- Co-authored-by: Daniel Karrasch <[email protected]> Co-authored-by: Oscar Smith <[email protected]>

This PR is a potential fix for #54833. ## Description The function https://github.com/JuliaLang/julia/blob/2a06376c18afd7ec875335070743dcebcd85dee7/stdlib/LinearAlgebra/src/triangular.jl#L2220 computes $\boldsymbol{A}^{\dfrac{1}{2^s}} - \boldsymbol{I}$ for a real-valued $2\times 2$ matrix $\boldsymbol{A}$ using Algorithm 5.1 in [R1]. However, the algorithm in [R1] as well as the above function do not handle the case $s=0.$ This fix extends the function to compute $\boldsymbol{A}^{\dfrac{1}{2^s}} - \boldsymbol{I} \Bigg|_{s=0} = \boldsymbol{A} - \boldsymbol{I}.$ ## Checklist - [X] Fix code: `stdlib\LinearAlgebra\src\triangular.jl` in function `_sqrt_pow_diag_block_2x2!(A, A0, s)`. - [X] Add test case: `stdlib\LinearAlgebra\test\triangular.jl`. - [X] Update `NEWS.md`. - [X] Testing and self review. | Tag | Reference | | --- | --- | | <nobr>[R1]</nobr> | Al-Mohy, Awad H. and Higham, Nicholas J. "Improved Inverse Scaling and Squaring Algorithms for the Matrix Logarithm", 2011, url: https://eprints.maths.manchester.ac.uk/1687/1/paper11.pdf | --------- Co-authored-by: Daniel Karrasch <[email protected]> Co-authored-by: Oscar Smith <[email protected]> (cherry picked from commit 2cdfe06)

oscardssmith added bug Indicates an unexpected problem or unintended behavior linear algebra Linear algebra labels Jun 17, 2024

oscardssmith mentioned this issue Jun 20, 2024

make log_quasitriu always take at least one sqrt #54867

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olivierverdier added a commit to olivierverdier/GeometricFilter.jl that referenced this issue Sep 9, 2024

Fix interpolation problem

c3638e1

Interpolation leads to the computation of `exp(log(v))` which is broken in Julia (JuliaLang/julia#54833)

aravindh-krishnamoorthy mentioned this issue Oct 24, 2024

Fix log_quasitriu for internal scaling s=0 #56311

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giordano linked a pull request Oct 24, 2024 that will close this issue

Fix log_quasitriu for internal scaling s=0 #56311

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oscardssmith closed this as completed in #56311 Oct 28, 2024

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Matrix `exp` and `log` are not inverse of each other #54833

Matrix `exp` and `log` are not inverse of each other #54833

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Matrix exp and log are not inverse of each other #54833

Matrix exp and log are not inverse of each other #54833

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Matrix `exp` and `log` are not inverse of each other #54833

Matrix `exp` and `log` are not inverse of each other #54833

olivierverdier commented Jun 17, 2024 •

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