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docs: fix external references
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docs/make.jl

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@@ -21,6 +21,7 @@ makedocs(; sitename = "NonlinearSolve.jl",
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modules = [NonlinearSolve, SimpleNonlinearSolve, SteadyStateDiffEq,
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Sundials, DiffEqBase, SciMLBase, SciMLJacobianOperators],
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clean = true,
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draft=true,
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doctest = false,
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linkcheck = true,
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linkcheck_ignore = ["https://twitter.com/ChrisRackauckas/status/1544743542094020615",

docs/src/basics/autodiff.md

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## Summary of Finite Differencing Backends
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- [`AutoFiniteDiff`](@extref ADTypes): Finite differencing using `FiniteDiff.jl`, not
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optimal but always applicable.
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- [`AutoFiniteDifferences`](@extref ADTypes): Finite differencing using
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`FiniteDifferences.jl`, not optimal but always applicable.
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- [`AutoFiniteDiff`](@extref ADTypes.AutoFiniteDiff): Finite differencing using
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`FiniteDiff.jl`, not optimal but always applicable.
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- [`AutoFiniteDifferences`](@extref ADTypes.AutoFiniteDifferences): Finite differencing
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using `FiniteDifferences.jl`, not optimal but always applicable.
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## Summary of Forward Mode AD Backends
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- [`AutoForwardDiff`](@extref ADTypes): The best choice for dense problems.
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- [`AutoPolyesterForwardDiff`](@extref ADTypes): Might be faster than
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[`AutoForwardDiff`](@extref ADTypes) for large problems. Requires
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- [`AutoForwardDiff`](@extref ADTypes.AutoForwardDiff): The best choice for dense
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problems.
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- [`AutoPolyesterForwardDiff`](@extref ADTypes.AutoPolyesterForwardDiff): Might be faster
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than [`AutoForwardDiff`](@extref ADTypes.AutoForwardDiff) for large problems. Requires
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`PolyesterForwardDiff.jl` to be installed and loaded.
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## Summary of Reverse Mode AD Backends
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- [`AutoZygote`](@extref ADTypes): The fastest choice for non-mutating array-based (BLAS)
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functions.
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- [`AutoEnzyme`](@extref ADTypes): Uses `Enzyme.jl` Reverse Mode and works for both
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in-place and out-of-place functions.
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- [`AutoZygote`](@extref ADTypes.AutoZygote): The fastest choice for non-mutating
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array-based (BLAS) functions.
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- [`AutoEnzyme`](@extref ADTypes.AutoEnzyme): Uses `Enzyme.jl` Reverse Mode and works for
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both in-place and out-of-place functions.
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!!! tip
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docs/src/tutorials/code_optimization.md

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Allocations are only expensive if they are “heap allocations”. For a more in-depth
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definition of heap allocations,
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[there are many sources online](http://net-informations.com/faq/net/stack-heap.htm).
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[there are many sources online](https://net-informations.com/faq/net/stack-heap.htm).
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But a good working definition is that heap allocations are variable-sized slabs of memory
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which have to be pointed to, and this pointer indirection costs time. Additionally, the heap
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has to be managed, and the garbage controllers has to actively keep track of what's on the

docs/src/tutorials/large_systems.md

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One of the useful companion tools for NonlinearSolve.jl is
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[ADTypes.jl](https://github.com/SciML/ADTypes.jl) that specifies the interface for sparsity
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detection via [`jacobian_sparsity`](@extref ADTypes). This allows for automatic
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declaration of Jacobian sparsity types. To see this in action, we can give an example `du`
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and `u` and call `jacobian_sparsity` on our function with the example arguments, and it will
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kick out a sparse matrix with our pattern, that we can turn into our `jac_prototype`.
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detection via [`jacobian_sparsity`](@extref ADTypes.jacobian_sparsity). This allows for
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automatic declaration of Jacobian sparsity types. To see this in action, we can give an
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example `du` and `u` and call `jacobian_sparsity` on our function with the example
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arguments, and it will kick out a sparse matrix with our pattern, that we can turn into our
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`jac_prototype`.
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!!! tip
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src/algorithms/extension_algs.jl

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@@ -317,7 +317,7 @@ NLSolversJL(; method, autodiff = nothing) = NLSolversJL(method, autodiff)
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SpeedMappingJL(; σ_min = 0.0, stabilize::Bool = false, check_obj::Bool = false,
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orders::Vector{Int} = [3, 3, 2], time_limit::Real = 1000)
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Wrapper over [SpeedMapping.jl](https://nicolasl-s.github.io/SpeedMapping.jl) for solving
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Wrapper over [SpeedMapping.jl](https://nicolasl-s.github.io/SpeedMapping.jl/) for solving
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Fixed Point Problems. We allow using this algorithm to solve root finding problems as well.
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### Keyword Arguments

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