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docs/src/api/nonlinearsolve.md

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## Polyalgorithms
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```@docs
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NonlinearSolvePolyAlgorithm
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FastShortcutNonlinearPolyalg
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FastShortcutNLLSPolyalg
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RobustMultiNewton
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```
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docs/src/solvers/NonlinearLeastSquaresSolvers.md

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## Recommended Methods
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`LevenbergMarquardt` is a good choice for most problems.
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The default method `FastShortcutNLLSPolyalg` is a good choice for most
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problems. It is a polyalgorithm that attempts to use a fast algorithm
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(`GaussNewton`) and if that fails it falls back to a more robust
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algorithm (`LevenbergMarquardt`).
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## Full List of Methods
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problems.
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- `SimpleNewtonRaphson()`: Simple Gauss Newton Implementation with `QRFactorization` to
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solve a linear least squares problem at each step!
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## Example usage
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```julia
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using NonlinearSolve
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sol = solve(prob, LevenbergMarquardt())
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```

test/23_test_problems.jl

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alg_ops = (GeneralBroyden(; max_resets = 10),)
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broken_tests = Dict(alg => Int[] for alg in alg_ops)
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broken_tests[alg_ops[1]] = [1, 2, 4, 5, 6, 11, 12, 13, 14, 22]
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broken_tests[alg_ops[1]] = [1, 2, 4, 5, 6, 11, 12, 13, 14]
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test_on_library(problems, dicts, alg_ops, broken_tests)
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end

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