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Merge pull request #801 from SciML/fix-sparsematrixcsc-typo
Fix typos and syntax errors in documentation
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docs/src/basics/faq.md

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@@ -179,7 +179,7 @@ for automatically transforming your equations.
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### I'm trying to solve DAEs but my solver is unstable and/or slow, what's wrong with IDA and DFBDF?
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Fully implicit DAEs ``f(du,u,p,t) = 0`` are extremely difficult to numerical handle for many reasons.
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Fully implicit DAEs ``f(du,u,p,t) = 0`` are extremely difficult to numerically handle for many reasons.
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The linearly implicit form ``Mu'=f(u)`` where ``M`` is a singular mass matrix is much simpler
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numerically and thus results in much better performance. This is seen in many instances with the
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SciMLBenchmarks. Thus it is recommended that in almost all or most situations, one should use the

docs/src/features/callback_functions.md

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@@ -137,7 +137,7 @@ of just `10`. This model is implemented as simply:
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```@example callback1
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dosetimes = [4.0, 6.0, 8.0]
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condition(u, t, integrator) = t ∈ dosetimes && (u[1] < 1.0)
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affect!(integrator) = integrator.u[1] += 10integrator.t
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affect!(integrator) = integrator.u[1] += 10 * integrator.t
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cb = DE.DiscreteCallback(condition, affect!)
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sol = DE.solve(prob, DE.Tsit5(), callback = cb, tstops = dosetimes)
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Plots.plot(sol)

docs/src/tutorials/advanced_ode_example.md

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@@ -258,7 +258,7 @@ by default). Then `newW = true` whenever a new `W` matrix is computed, and
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`newW === nothing || newW` and when true, it's only at these points when
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we update the preconditioner, otherwise we just pass on the previous version.
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We use `convert(AbstractMatrix,W)` to get the concrete `W` matrix (matching
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`jac_prototype`, thus `SpraseMatrixCSC`) which we can use in the preconditioner's
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`jac_prototype`, thus `SparseMatrixCSC`) which we can use in the preconditioner's
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definition. Then we use `IncompleteLU.ilu` on that sparse matrix to generate
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the preconditioner. We return `Pl,nothing` to say that our preconditioner is a
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left preconditioner, and that there is no right preconditioning.

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