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The implicit layers approach to calculating gradients requires solving a linear system with A::Matrix \ x::Vector
where A
is the Jacobian dG/d(u,p)
where G = [ F, det(J) ]
. Let's make a plot of the percentage error between finite difference and autodiff as the condition number for A
is increased. @rveltz I use the same linear solvers as you, do you know what happens when your matrix J = dF/du
is ill-conditioned?
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