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GaussNewton algorithm fails with sparse Jacobian #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 👇

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
  [a93c6f00] DataFrames v1.7.0
  [864edb3b] DataStructures v0.18.20
  [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
⌃ [429591f6] ExactPredicates v2.2.5
  [d4d017d3] ExponentialUtilities v1.27.0
  [e2ba6199] ExprTools v0.1.10
  [55351af7] ExproniconLite v0.10.14
  [411431e0] Extents v0.1.5
  [7a1cc6ca] FFTW v1.8.1
  [9d29842c] FastAlmostBandedMatrices v0.1.4
  [7034ab61] FastBroadcast v0.3.5
  [9aa1b823] FastClosures v0.3.2
  [442a2c76] FastGaussQuadrature v1.0.2
  [a4df4552] FastPower v1.1.1
  [fa42c844] FastRounding v0.3.1
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  [1a297f60] FillArrays v1.13.0
  [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
⌅ [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
⌅ [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 ⌃ and ⌅ have new versions available. Those with ⌃ may be upgradable, but those with ⌅ 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 × 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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