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docs/src/tutorials/ode_modeling.md

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@@ -326,12 +326,11 @@ prob_an = ODEProblem(connected_simp, u0, (0.0,10.0), p; jac=true, sparse=true)
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The speedup is significant. For this small dense model (3 of 4 entries are
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populated), using sparse matrices is counterproductive in terms of required
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memory allocations. For large,
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hierarchically built models, which tend to be sparse, speedup and the reduction of
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memory allocation can be expected to be substantial. In addition, these problem
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builders allow for automatic parallelism using the structural information. For
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more information, see the [ODESystem](@ref ODESystem) page.
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memory allocations. For large, hierarchically built models, which tend to be
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sparse, speedup and the reduction of memory allocation can be expected to be
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substantial. In addition, these problem builders allow for automatic parallelism
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using the structural information. For more information, see the
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[ODESystem](@ref ODESystem) page.
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## Notes and pointers how to go on
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