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GaussNewton algorithm fails with sparse JacobianΒ #535

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@afossa

Description

@afossa

Describe the bug 🐞

I am trying to solve a nonlinear least squares problem using the GaussNewton algorithm. For this, I first build a NonlinearFunction object and provide it a function that evaluates the analytical Jacobian via the keyword jac, as well as the sparsity pattern of the Jacobian as a SparseMatrixCSC via the keyword jac_prototype.

With these settings, the algorithm fails to find a solution. However, the same algorithm succeeds when using a dense Jacobian, i.e. jac_prototype=nothing. In both cases, the function passed to jac is the same.

The problem is also correctly solved with both sparse and dense Jacobians by LevenbergMarquardt, and by a simple implementation of the Gauss-Newton algorithm that I wrote myself.

Expected behavior

The algorithm should find the same solution using either the sparse or dense Jacobian.

Minimal Reproducible Example πŸ‘‡

The code is quite involved and I cannot easily share a MWE. Here is how I invoke NonlinearSolve:

# define the nonlinear function
nlf = NonlinearFunction(
	(y, u, p) -> compute_residuals!(y, u, f, p);
	jac=(j, u, p) -> compute_jacobian!(j, u, f, p),
	resid_prototype=zeros(eltype(z), 7 * (length(t) - 1)),
	jac_prototype=sparse ? build_jacobian_prototype(length(t), eltype(z)) : nothing,
)

# setup the nonlinear least squares problem
nlls = NonlinearLeastSquaresProblem(nlf, z, p)

# solve the problem
sol = solve(
	nlls, alg; abstol=abstol, reltol=reltol, maxiters=maxiters, store_trace=Val(true)
)

And this is my implementation of the Gauss-Newton algorithm that works as expected:

# preallocate the residuals vector and the Jacobian matrix
y = Vector{eltype(z)}(undef, 7 * (n - 1))
J = sparse ? build_jacobian_prototype(n, eltype(z)) : zeros(eltype(z), 7 * (n - 1), 8 * n - 1)

# perform differential correction
@printf "\n%-6s %-12s\n" "Iter" "||F(X)||"
iter = 1
while iter <= maxiters

	# compute the residuals and the Jacobian
	compute_residuals!(y, z, f, p)
	compute_jacobian!(J, z, f, p)

	# compute the error
	err = norm(y)
	@printf "%-6d %-12.3e\n" iter err
	if err < tol
	    break
	end

	# compute the differential correction
	z -= J' * ((J * J') \ y)
	iter += 1
end

Error & Stacktrace ⚠️

Below are the outputs that I obtain with the various solvers. For NonlinearSolve algorithms, the output is sol.trace.

Custom Gauss-Newton with sparse Jacobian

Iter   ||F(X)||    
1      1.149e-02   
2      3.279e-02   
3      7.333e-04   
4      9.491e-05   
5      1.152e-07   

Custom Gauss-Newton with dense Jacobian

Iter   ||F(X)||    
1      1.149e-02   
2      3.279e-02   
3      7.333e-04   
4      9.491e-05   
5      1.152e-07   

LevenbergMarquardt with sparse Jacobian

----            -------------           -----------         
Iter            f(u) 2-norm             Step 2-norm         
----            -------------           -----------         
0               1.14892278e-02          NaN                 
1               7.82122797e-03          3.07284847e-03      
2               4.69430204e-03          3.89589597e-03      
3               2.42796810e-03          4.11015131e-03      
4               1.38073389e-03          3.23202627e-03      
5               9.68211587e-04          2.90107179e-03      
6               7.47923220e-04          3.31432763e-03      
7               6.05150206e-04          3.97634536e-03      
8               5.46399783e-04          3.79915113e-03      
9               5.16374874e-04          5.31463642e-03      
10              4.67425117e-04          1.17584536e-02      
11              3.76419623e-04          2.46911130e-02      
12              2.45198956e-04          4.00718276e-02      
13              1.42132618e-04          4.07294052e-02      
14              1.05357204e-04          3.07927619e-02      
15              8.10347038e-05          4.44443269e-02      
16              6.18903157e-05          5.47829524e-02      
17              4.89811537e-05          8.83248604e-02      
18              6.10211165e-05          1.56004315e-01      
19              8.05625238e-05          1.62644806e-01      
20              1.87248035e-05          8.71631471e-02      
21              3.03098412e-06          3.68944850e-02      
22              1.36667772e-06          3.24970643e-02      
23              3.87400563e-07          1.99830078e-02      

LevenbergMarquardt with dense Jacobian

----            -------------           -----------         
Iter            f(u) 2-norm             Step 2-norm         
----            -------------           -----------         
0               1.14892278e-02          0.00000000e+00      
1               7.82122797e-03          3.07284847e-03      
2               4.69430209e-03          3.89589595e-03      
3               2.42796809e-03          4.11015131e-03      
4               1.38073389e-03          3.23202639e-03      
5               9.68211582e-04          2.90107164e-03      
6               7.47923219e-04          3.31432774e-03      
7               6.05150207e-04          3.97634546e-03      
8               5.46399786e-04          3.79915117e-03      
9               5.16374874e-04          5.31463636e-03      
10              4.67425118e-04          1.17584533e-02      
11              3.76419618e-04          2.46911127e-02      
12              2.45198926e-04          4.00718275e-02      
13              1.42132586e-04          4.07294058e-02      
14              1.05357204e-04          3.07927620e-02      
15              8.10351664e-05          4.44443276e-02      
16              6.18909318e-05          5.47829521e-02      
17              4.89802421e-05          8.83248676e-02      
18              6.10196967e-05          1.56004315e-01      
19              8.05651899e-05          1.62644804e-01      
20              1.87267150e-05          8.71631544e-02      
21              3.04197992e-06          3.68945058e-02      
22              1.36714498e-06          3.24970909e-02      
23              3.77087784e-07          1.99829902e-02      

GaussNewton with sparse Jacobian

----            -------------           -----------         
Iter            f(u) 2-norm             Step 2-norm         
----            -------------           -----------         
0               1.14892278e-02          NaN                 
1               1.02361359e+03          9.32597506e+02      
2               7.52268333e+13          1.65378316e+15      
3               7.51892654e+13          1.69735680e-01      
4               7.51892654e+13          1.11035748e-06      
5               7.51892654e+13          1.93772018e-11      
6               7.51892654e+13          2.17817614e-12      
7               7.51892654e+13          2.29011151e-12      
8               7.51892654e+13          2.46344705e-12      
9               7.51892654e+13          2.24889121e-12      
10              7.51892654e+13          2.19226658e-12      
11              7.51892654e+13          2.39834777e-12      
12              7.51892654e+13          2.20819525e-12      
13              7.51892654e+13          2.15383011e-12      
14              7.51892654e+13          2.43979121e-12      
15              7.51892654e+13          2.20590714e-12      
16              7.51892654e+13          2.45688592e-12      
17              7.51892654e+13          2.25495804e-12      
18              7.51892654e+13          2.27461017e-12      
19              7.51892654e+13          2.20360961e-12      
20              7.51892654e+13          2.35326923e-12      
21              7.51892654e+13          2.30048104e-12      
22              7.51892654e+13          2.26073700e-12      
23              7.51892654e+13          2.38989481e-12      
24              7.51892654e+13          2.20973979e-12      
25              7.51892654e+13          2.37065847e-12      
26              7.51892654e+13          2.06767767e-12      
27              7.51892654e+13          2.31639324e-12      
28              7.51892654e+13          2.39756280e-12      
29              7.51892654e+13          2.25616631e-12      
30              7.51892654e+13          2.20151345e-12      
31              7.51892654e+13          2.33937500e-12      
32              7.51892654e+13          2.44461873e-12      
33              7.51892654e+13          2.01244245e-12      
34              7.51892654e+13          2.39902907e-12      
35              7.51892654e+13          2.23555846e-12      
36              1.14892278e-02          2.16666149e-12      

GaussNewton with dense Jacobian

----            -------------           -----------         
Iter            f(u) 2-norm             Step 2-norm         
----            -------------           -----------         
0               1.14892278e-02          NaN                 
1               3.27883611e-02          5.00658383e-01      
2               7.33302702e-04          3.91702511e-02      
3               9.49118434e-05          2.29373614e-02      
4               1.15164439e-07          6.68932773e-04  

Environment (please complete the following information):

  • Output of using Pkg; Pkg.status()
Status `/workspace/dev/trajectories/Project.toml`
  [59872b50] AstroConstants v0.1.0
  [6e4b80f9] BenchmarkTools v1.6.0
  [336ed68f] CSV v0.10.15
  [13f3f980] CairoMakie v0.13.2
  [35d6a980] ColorSchemes v3.29.0
  [a93c6f00] DataFrames v1.7.0
  [2b5f629d] DiffEqBase v6.164.1
  [163ba53b] DiffResults v1.1.0
  [b552c78f] DiffRules v1.15.1
  [0c46a032] DifferentialEquations v7.16.0
  [6a9c3322] Ephemerides v1.2.1
  [6a86dc24] FiniteDiff v2.27.0
  [f6369f11] ForwardDiff v0.10.38
  [5be70612] FrameTransformations v3.0.0
  [e9467ef8] GLMakie v0.11.3
  [5c1252a2] GeometryBasics v0.5.5
  [6218d12a] ImageMagick v1.4.0
  [6b30ee2f] JSMDInterfaces v1.6.0
  [4076af6c] JuMP v1.24.0
  [4854310b] MINPACK v1.3.0
  [1ec41992] MosekTools v0.15.5
  [8913a72c] NonlinearSolve v4.4.0
  [74f56ac7] ReferenceFrameRotations v3.0.2
  [b792745b] SMDGraphs v0.2.0
  [90137ffa] StaticArrays v1.9.12
  [6aa5eb33] TaylorSeries v0.18.3
  [c33777b2] Tempo v1.3.1
  [de0858da] Printf v1.11.0
  • Output of using Pkg; Pkg.status(; mode = PKGMODE_MANIFEST)
Status `/workspace/dev/trajectories/Manifest.toml`
  [47edcb42] ADTypes v1.13.0
  [621f4979] AbstractFFTs v1.5.0
  [1520ce14] AbstractTrees v0.4.5
  [7d9f7c33] Accessors v0.1.42
  [79e6a3ab] Adapt v4.2.0
  [35492f91] AdaptivePredicates v1.2.0
  [66dad0bd] AliasTables v1.1.3
  [a95523ee] AlmostBlockDiagonals v0.1.10
  [27a7e980] Animations v0.4.2
  [ec485272] ArnoldiMethod v0.4.0
  [4fba245c] ArrayInterface v7.18.0
  [4c555306] ArrayLayouts v1.11.1
  [59872b50] AstroConstants v0.1.0
  [67c07d97] Automa v1.1.0
  [13072b0f] AxisAlgorithms v1.1.0
  [39de3d68] AxisArrays v0.4.7
  [aae01518] BandedMatrices v1.9.2
  [6e4b80f9] BenchmarkTools v1.6.0
  [62783981] BitTwiddlingConvenienceFunctions v0.1.6
  [764a87c0] BoundaryValueDiffEq v5.15.0
  [7227322d] BoundaryValueDiffEqAscher v1.4.0
  [56b672f2] BoundaryValueDiffEqCore v1.7.0
  [85d9eb09] BoundaryValueDiffEqFIRK v1.5.0
  [1a22d4ce] BoundaryValueDiffEqMIRK v1.5.0
  [9255f1d6] BoundaryValueDiffEqMIRKN v1.4.0
  [ed55bfe0] BoundaryValueDiffEqShooting v1.5.0
  [70df07ce] BracketingNonlinearSolve v1.1.0
  [fa961155] CEnum v0.5.0
  [2a0fbf3d] CPUSummary v0.2.6
  [96374032] CRlibm v1.0.2
  [336ed68f] CSV v0.10.15
  [159f3aea] Cairo v1.1.1
  [13f3f980] CairoMakie v0.13.2
  [d360d2e6] ChainRulesCore v1.25.1
  [fb6a15b2] CloseOpenIntervals v0.1.13
  [523fee87] CodecBzip2 v0.8.5
  [944b1d66] CodecZlib v0.7.8
  [a2cac450] ColorBrewer v0.4.1
  [35d6a980] ColorSchemes v3.29.0
  [3da002f7] ColorTypes v0.12.0
  [c3611d14] ColorVectorSpace v0.11.0
  [5ae59095] Colors v0.13.0
  [38540f10] CommonSolve v0.2.4
  [bbf7d656] CommonSubexpressions v0.3.1
  [f70d9fcc] CommonWorldInvalidations v1.0.0
  [34da2185] Compat v4.16.0
  [a33af91c] CompositionsBase v0.1.2
  [2569d6c7] ConcreteStructs v0.2.3
  [187b0558] ConstructionBase v1.5.8
  [d38c429a] Contour v0.6.3
  [adafc99b] CpuId v0.3.1
  [a8cc5b0e] Crayons v4.1.1
  [9a962f9c] DataAPI v1.16.0
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  [e2d170a0] DataValueInterfaces v1.0.0
  [927a84f5] DelaunayTriangulation v1.6.4
  [bcd4f6db] DelayDiffEq v5.52.0
  [8bb1440f] DelimitedFiles v1.9.1
  [2b5f629d] DiffEqBase v6.164.1
  [459566f4] DiffEqCallbacks v4.3.0
  [77a26b50] DiffEqNoiseProcess v5.24.1
  [163ba53b] DiffResults v1.1.0
  [b552c78f] DiffRules v1.15.1
  [0c46a032] DifferentialEquations v7.16.0
  [a0c0ee7d] DifferentiationInterface v0.6.42
  [b4f34e82] Distances v0.10.12
  [31c24e10] Distributions v0.25.117
  [ffbed154] DocStringExtensions v0.9.3
  [4e289a0a] EnumX v1.0.4
  [f151be2c] EnzymeCore v0.8.8
  [6a9c3322] Ephemerides v1.2.1
  [90fa49ef] ErrorfreeArithmetic v0.5.2
βŒƒ [429591f6] ExactPredicates v2.2.5
  [d4d017d3] ExponentialUtilities v1.27.0
  [e2ba6199] ExprTools v0.1.10
  [55351af7] ExproniconLite v0.10.14
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  [7a1cc6ca] FFTW v1.8.1
  [9d29842c] FastAlmostBandedMatrices v0.1.4
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  [9aa1b823] FastClosures v0.3.2
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  [6a86dc24] FiniteDiff v2.27.0
  [53c48c17] FixedPointNumbers v0.8.5
  [1fa38f19] Format v1.3.7
  [f6369f11] ForwardDiff v0.10.38
  [5be70612] FrameTransformations v3.0.0
  [b38be410] FreeType v4.1.1
  [663a7486] FreeTypeAbstraction v0.10.6
  [069b7b12] FunctionWrappers v1.1.3
  [77dc65aa] FunctionWrappersWrappers v0.1.3
  [d9f16b24] Functors v0.5.2
  [f7f18e0c] GLFW v3.4.3
  [e9467ef8] GLMakie v0.11.3
  [46192b85] GPUArraysCore v0.2.0
  [c145ed77] GenericSchur v0.5.4
  [68eda718] GeoFormatTypes v0.4.4
  [cf35fbd7] GeoInterface v1.4.1
  [5c1252a2] GeometryBasics v0.5.5
  [a2bd30eb] Graphics v1.1.3
  [86223c79] Graphs v1.12.0
  [3955a311] GridLayoutBase v0.11.1
  [42e2da0e] Grisu v1.0.2
  [34004b35] HypergeometricFunctions v0.3.27
  [4e86e20e] IERSConventions v1.1.2
  [615f187c] IfElse v0.1.1
  [2803e5a7] ImageAxes v0.6.12
  [c817782e] ImageBase v0.1.7
  [a09fc81d] ImageCore v0.10.5
  [82e4d734] ImageIO v0.6.9
  [6218d12a] ImageMagick v1.4.0
  [bc367c6b] ImageMetadata v0.9.10
  [9b13fd28] IndirectArrays v1.0.0
  [d25df0c9] Inflate v0.1.5
  [842dd82b] InlineStrings v1.4.3
  [a98d9a8b] Interpolations v0.15.1
βŒ… [d1acc4aa] IntervalArithmetic v0.20.9
  [8197267c] IntervalSets v0.7.10
  [3587e190] InverseFunctions v0.1.17
  [41ab1584] InvertedIndices v1.3.1
  [92d709cd] IrrationalConstants v0.2.4
  [f1662d9f] Isoband v0.1.1
  [c8e1da08] IterTools v1.10.0
  [82899510] IteratorInterfaceExtensions v1.0.0
  [692b3bcd] JLLWrappers v1.7.0
  [6b30ee2f] JSMDInterfaces v1.6.0
  [67801824] JSMDUtils v1.2.1
  [682c06a0] JSON v0.21.4
  [0f8b85d8] JSON3 v1.14.1
  [ae98c720] Jieko v0.2.1
  [b835a17e] JpegTurbo v0.1.5
  [4076af6c] JuMP v1.24.0
  [ccbc3e58] JumpProcesses v9.14.2
  [5ab0869b] KernelDensity v0.6.9
  [ba0b0d4f] Krylov v0.9.10
  [b964fa9f] LaTeXStrings v1.4.0
  [10f19ff3] LayoutPointers v0.1.17
  [5078a376] LazyArrays v2.6.1
  [8cdb02fc] LazyModules v0.3.1
  [2d8b4e74] LevyArea v1.0.0
  [87fe0de2] LineSearch v0.1.4
  [d3d80556] LineSearches v7.3.0
  [7ed4a6bd] LinearSolve v3.3.1
  [2ab3a3ac] LogExpFunctions v0.3.29
  [4854310b] MINPACK v1.3.0
  [1914dd2f] MacroTools v0.5.15
  [ee78f7c6] Makie v0.22.2
  [20f20a25] MakieCore v0.9.1
  [d125e4d3] ManualMemory v0.1.8
  [dbb5928d] MappedArrays v0.4.2
  [b8f27783] MathOptInterface v1.37.0
  [0a4f8689] MathTeXEngine v0.6.2
  [a3b82374] MatrixFactorizations v3.0.1
  [bb5d69b7] MaybeInplace v0.1.4
  [7269a6da] MeshIO v0.5.2
  [e1d29d7a] Missings v1.2.0
  [66fc600b] ModernGL v1.1.8
  [e94cdb99] MosaicViews v0.3.4
βŒ… [6405355b] Mosek v10.2.0
  [1ec41992] MosekTools v0.15.5
  [2e0e35c7] Moshi v0.3.5
  [46d2c3a1] MuladdMacro v0.2.4
  [d8a4904e] MutableArithmetics v1.6.4
  [d41bc354] NLSolversBase v7.8.3
  [2774e3e8] NLsolve v4.5.1
  [77ba4419] NaNMath v1.1.2
  [f09324ee] Netpbm v1.1.1
  [8913a72c] NonlinearSolve v4.4.0
  [be0214bd] NonlinearSolveBase v1.5.0
  [5959db7a] NonlinearSolveFirstOrder v1.3.0
  [9a2c21bd] NonlinearSolveQuasiNewton v1.2.0
  [26075421] NonlinearSolveSpectralMethods v1.1.0
  [510215fc] Observables v0.5.5
  [6fe1bfb0] OffsetArrays v1.15.0
  [52e1d378] OpenEXR v0.3.3
  [429524aa] Optim v1.11.0
  [bac558e1] OrderedCollections v1.8.0
  [1dea7af3] OrdinaryDiffEq v6.91.0
  [89bda076] OrdinaryDiffEqAdamsBashforthMoulton v1.2.0
  [6ad6398a] OrdinaryDiffEqBDF v1.2.0
  [bbf590c4] OrdinaryDiffEqCore v1.18.1
  [50262376] OrdinaryDiffEqDefault v1.3.0
  [4302a76b] OrdinaryDiffEqDifferentiation v1.4.0
  [9286f039] OrdinaryDiffEqExplicitRK v1.1.0
  [e0540318] OrdinaryDiffEqExponentialRK v1.3.0
  [becaefa8] OrdinaryDiffEqExtrapolation v1.4.0
  [5960d6e9] OrdinaryDiffEqFIRK v1.7.0
  [101fe9f7] OrdinaryDiffEqFeagin v1.1.0
  [d3585ca7] OrdinaryDiffEqFunctionMap v1.1.1
  [d28bc4f8] OrdinaryDiffEqHighOrderRK v1.1.0
  [9f002381] OrdinaryDiffEqIMEXMultistep v1.2.0
  [521117fe] OrdinaryDiffEqLinear v1.1.0
  [1344f307] OrdinaryDiffEqLowOrderRK v1.2.0
  [b0944070] OrdinaryDiffEqLowStorageRK v1.2.1
  [127b3ac7] OrdinaryDiffEqNonlinearSolve v1.5.0
  [c9986a66] OrdinaryDiffEqNordsieck v1.1.0
  [5dd0a6cf] OrdinaryDiffEqPDIRK v1.2.0
  [5b33eab2] OrdinaryDiffEqPRK v1.1.0
  [04162be5] OrdinaryDiffEqQPRK v1.1.0
  [af6ede74] OrdinaryDiffEqRKN v1.1.0
  [43230ef6] OrdinaryDiffEqRosenbrock v1.6.0
  [2d112036] OrdinaryDiffEqSDIRK v1.2.0
  [669c94d9] OrdinaryDiffEqSSPRK v1.2.0
  [e3e12d00] OrdinaryDiffEqStabilizedIRK v1.2.0
  [358294b1] OrdinaryDiffEqStabilizedRK v1.1.0
  [fa646aed] OrdinaryDiffEqSymplecticRK v1.3.0
  [b1df2697] OrdinaryDiffEqTsit5 v1.1.0
  [79d7bb75] OrdinaryDiffEqVerner v1.1.1
  [90014a1f] PDMats v0.11.32
  [f57f5aa1] PNGFiles v0.4.4
  [65ce6f38] PackageExtensionCompat v1.0.2
  [19eb6ba3] Packing v0.5.1
  [5432bcbf] PaddedViews v0.5.12
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Info Packages marked with βŒƒ and βŒ… have new versions available. Those with βŒƒ may be upgradable, but those with βŒ… are restricted by compatibility constraints from upgrading. To see why use `status --outdated -m`
  • Output of versioninfo()
Julia Version 1.11.3
Commit d63adeda50d (2025-01-21 19:42 UTC)
Build Info:
  Official https://julialang.org/ release
Platform Info:
  OS: Linux (x86_64-linux-gnu)
  CPU: 24 Γ— 13th Gen Intel(R) Core(TM) i7-13700
  WORD_SIZE: 64
  LLVM: libLLVM-16.0.6 (ORCJIT, alderlake)
Threads: 1 default, 0 interactive, 1 GC (on 24 virtual cores)
Environment:
  JULIA_DEPOT_PATH = /workspace/julia
  JULIA_EDITOR = code
  JULIA_NUM_THREADS = 

Additional context

As you can see, the Gauss-Newton algorithm is perfectly capable to solve the problem, and it does so in much less iteration than Levenberg-Marquardt. However, the implementation in NonlinearSolve fails when specifying a sparse analytical Jacobian.

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