Add_minimalsurface #182
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tmigot
requested changes
May 26, 2022
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Thanks @indyalardjane for the PR!
I added a couple of comments.
In the functions, you don't want to convert variables with T as it will break the derivatives. We want Julia to infer the type.
Do you have bound constraints in your model as well? I.e. constraints of the form x >= lvar or x <= uvar? If so, you should prepare vector lvar and uvar and use the following constructor
ADNLPModel(f, x0, lvar, uvar, c, lcon, ucon)
tmigot
requested changes
Jun 7, 2022
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| using Plots | ||
| using ADNLPModels, NLPModels, NLPModelsIpopt, DataFrames, LinearAlgebra, Distances, SolverCore, PyPlot |
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| using Plots | |
| using ADNLPModels, NLPModels, NLPModelsIpopt, DataFrames, LinearAlgebra, Distances, SolverCore, PyPlot |
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| v = vec(v_D) #convert to vector | ||
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| return ADNLPModel(f, v, constraints, lcon, ucon) |
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| return ADNLPModel(f, v, constraints, lcon, ucon) | |
| return ADNLPModels.ADNLPModel(f, v, constraints, lcon, ucon) |
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| s = hx * hy * (sqrt(1 + (gi^2) +(gj)^2)) # Approximation of the derivative with the method of rectangles | ||
| S = S + s # Update the value of S |
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| s = hx * hy * (sqrt(1 + (gi^2) +(gj)^2)) # Approximation of the derivative with the method of rectangles | |
| S = S + s # Update the value of S | |
| S = S + hx * hy * (sqrt(1 + (gi^2) +(gj)^2)) # Approximation of the derivative with the method of rectangles |
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| nx = 20 # number of points according to the direction x | ||
| ny = 20 # number of points according to the direction y |
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Is there a reason to choose 20? I suggest we do: nx = Int(round(sqrt(n))) and ny = nx
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@indyalardjane It looks like there are still a few comments to address here, please. |
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