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I want to use NMaximize to find the maximum value given by one interpolation function with respect to a constraint given by a second interpolation function. The main problem is that NMaximize produces a warning when running that says "InterpolatingFunction::dmval: Input value {value} lies outside the range of data in the interpolating function. Extrapolation will be used." I want to force NMaximize to only work with points inside the domain where interpolation can be used so that extrapolation is avoided. I attempted to do this by adding inequalities to the constraints that indicate the values in the region where the interpolation was done and also by forcing NMaximize to start near where I suspect the optimal value to be.

I've tried this in the following way:

f1 = Interpolation[logdata1, InterpolationOrder -> 3];
f2 = Interpolation[data2, InterpolationOrder -> 3];
point = Import["inits.txt", "Table"];
NMaximize[{f2[x, y, z], f1[x, y, z] <= Log[0.5], 4 <= x <= 5.5, 
  0.5 <= y <= 2, 0.4 <= z <= 1.07}, {x, y, z}, 
 Method -> {"Automatic", "InitialPoints" -> point}]

"point" is just a table with one row containing the values 4.5,1.25,and 0.76. This is what I determined the optimal discrete value to be so I want to use it as a start point. The region for interpolation is given by 4 <= x <= 5.5,0.5 <= y <= 2, 0.4 <= z <= 1.07

I've also tried the following to force NMaximize to stay within the interpolation domain:

R1 = ConvexHullMesh[logdata1[[All, 1]]];
R2 = ConvexHullMesh[data2[[All, 1]]];
R = RegionIntersection[R1, R2];
NMaximize[{f2[x, y, z], f1[x, y, z] <= Log[0.5]}, {x, y, z} \[Element] R, 
 Method -> {"Automatic", "InitialPoints" -> point}]

But this actually gives the same exact results as the above code and has the same issue.

My main questions are:

  1. How can I force NMaximize to only work with values within the region of interpolation?
  2. Why do I get a different answer when I use interpolation order of 2 for f1 instead of 3?
  3. How do I know that I can trust the accuracy of the interpolating functions/optimization?
  4. Any suggestions for visualizing the results?

I know that similar posts have been listed in the comments but I don't fully understand either method proposed. Any help either understanding those posts or finding other methods is very appreciated. Thank you!

Below is the data that I used in case it is needed:

logdata1 = {{{4.0, 1.25, 0.45}, 
    2.4476110710053582}, {{4.0, 2.0, 
     0.45}, -0.4760070040170787}, {{4.5, 1.25, 0.45}, 
    2.1639608012496088}, {{4.5, 2.0, 0.45}, 
    0.5303864073024669}, {{5.0, 1.25, 0.45}, 
    1.9622857635137856}, {{5.0, 2.0, 0.45}, 
    1.9488846460698512}, {{5.5, 1.25, 0.45}, 
    1.8139919050322073}, {{5.5, 2.0, 
     0.45}, -0.9527028993939473}, {{4.0, 0.5, 0.605}, 
    4.059260222075511}, {{4.0, 1.25, 0.605}, 
    0.9332419979067259}, {{4.0, 2.0, 
     0.605}, -0.9612143012617799}, {{4.5, 0.5, 0.605}, 
    4.850402604347952}, {{4.5, 1.25, 0.605}, 
    0.6549376656053791}, {{4.5, 2.0, 
     0.605}, -0.27196613541994935}, {{5.0, 1.25, 0.605}, 
    0.4669707790329627}, {{5.0, 2.0, 0.605}, 
    0.9180799618231699}, {{5.5, 0.5, 0.605}, 
    4.531939978304144}, {{5.5, 1.25, 0.605}, 
    0.3212005070088676}, {{5.5, 2.0, 
     0.605}, -2.0627765675879792}, {{4.0, 0.5, 0.76}, 
    2.4284995329088637}, {{4.0, 1.25, 
     0.76}, -0.1051143992907932}, {{4.0, 2.0, 
     0.76}, -0.8832538784145662}, {{4.5, 0.5, 0.76}, 
    3.1644735438592906}, {{4.5, 1.25, 
     0.76}, -0.7288545733720814}, {{4.5, 2.0, 
     0.76}, -1.1551175665109155}, {{5.0, 0.5, 0.76}, 
    4.518119732686612}, {{5.0, 1.25, 
     0.76}, -0.9533340580032796}, {{5.0, 2.0, 
     0.76}, -0.18352510547611892}, {{5.5, 0.5, 0.76}, 
    2.8849673931650806}, {{5.5, 1.25, 
     0.76}, -1.08780166364667}, {{5.5, 2.0, 
     0.76}, -3.1244191934660304}, {{4.0, 0.5, 0.915}, 
    0.7786084895594321}, {{4.0, 1.25, 
     0.915}, -0.8728949665399017}, {{4.0, 2.0, 
     0.915}, -1.17603596087305}, {{4.5, 0.5, 0.915}, 
    2.1786298628411367}, {{4.5, 1.25, 
     0.915}, -1.6388818066168709}, {{4.5, 2.0, 
     0.915}, -1.55409279971176}, {{5.0, 0.5, 0.915}, 
    1.402255994802939}, {{5.0, 1.25, 
     0.915}, -2.1364423513977218}, {{5.0, 2.0, 
     0.915}, -2.2235681116987904}, {{5.5, 0.5, 0.915}, 
    1.208128358101143}, {{5.5, 1.25, 
     0.915}, -2.5425674236742957}, {{5.5, 2.0, 
     0.915}, -4.462884577816279}, {{4.0, 0.5, 
     1.07}, -0.1500180481707993}, {{4.0, 1.25, 
     1.07}, -0.8100261071745211}, {{4.0, 2.0, 
     1.07}, -0.8624884008823027}, {{4.5, 0.5, 
     1.07}, -1.1169819037162614}, {{4.5, 1.25, 
     1.07}, -2.52249142770252}, {{4.5, 2.0, 
     1.07}, -2.6378648565587306}, {{5.0, 0.5, 
     1.07}, -1.4465141616100985}, {{5.0, 1.25, 
     1.07}, -3.335689123362051}, {{5.0, 2.0, 
     1.07}, -3.769595502304815}, {{5.5, 0.5, 
     1.07}, -1.4758738651819627}, {{5.5, 1.25, 
     1.07}, -3.7132682217485584}, {{5.5, 2.0, 
     1.07}, -4.654076972414527}, {{4.0, 0.5, 0.45}, 
    5.353657461581201}, {{4.5, 0.5, 0.45}, 
    6.465506884780405}, {{5.0, 0.5, 0.45}, 
    7.412341477958108}, {{5.0, 0.5, 0.605}, 
    6.133636626247068}, {{5.5, 0.5, 0.45}, 6.132768777805711}};

data2 = {{{4.0, 1.25, 0.45}, 
    827963.4278091961}, {{4.0, 2.0, 0.45}, 
    600968.389587756}, {{4.5, 1.25, 0.45}, 
    811646.576130428}, {{4.5, 2.0, 0.45}, 
    581220.328739186}, {{5.0, 1.25, 0.45}, 
    800973.007076825}, {{5.0, 2.0, 0.45}, 
    568570.203769995}, {{5.5, 1.25, 0.45}, 
    794017.756771696}, {{5.5, 2.0, 0.45}, 
    560213.396935101}, {{4.0, 0.5, 0.605}, 
    838950.27758945}, {{4.0, 1.25, 0.605}, 
    688775.333493315}, {{4.0, 2.0, 0.605}, 
    561752.068527685}, {{4.5, 0.5, 0.605}, 
    817733.35451236}, {{4.5, 1.25, 0.605}, 
    666198.092749207}, {{4.5, 2.0, 0.605}, 
    536817.008337762}, {{5.0, 1.25, 0.605}, 
    651170.6667152981}, {{5.0, 2.0, 0.605}, 
    520182.0224774009}, {{5.5, 0.5, 0.605}, 
    793588.1419891589}, {{5.5, 1.25, 0.605}, 
    640681.7856759089}, {{5.5, 2.0, 0.605}, 
    508701.99849301507}, {{4.0, 0.5, 0.76}, 
    689654.996746875}, {{4.0, 1.25, 0.76}, 
    611683.167247716}, {{4.0, 2.0, 0.76}, 
    531730.603245925}, {{4.5, 0.5, 0.76}, 
    663248.496983644}, {{4.5, 1.25, 0.76}, 
    584549.964560992}, {{4.5, 2.0, 0.76}, 
    502723.19077599095}, {{5.0, 0.5, 0.76}, 
    644962.463115578}, {{5.0, 1.25, 0.76}, 
    565791.185474893}, {{5.0, 2.0, 0.76}, 
    482696.72586706094}, {{5.5, 0.5, 0.76}, 
    632120.948971156}, {{5.5, 1.25, 0.76}, 
    552439.312601659}, {{5.5, 2.0, 0.76}, 
    468594.012480716}, {{4.0, 0.5, 0.915}, 
    608517.065299336}, {{4.0, 1.25, 0.915}, 
    562326.6690796369}, {{4.0, 2.0, 0.915}, 
    508709.0361146391}, {{4.5, 0.5, 0.915}, 
    578138.227790258}, {{4.5, 1.25, 0.915}, 
    531370.257938272}, {{4.5, 2.0, 0.915}, 
    476325.162144433}, {{5.0, 0.5, 0.915}, 
    556771.4056356569}, {{5.0, 1.25, 0.915}, 
    509519.839716492}, {{5.0, 2.0, 0.915}, 
    453514.3685128}, {{5.5, 0.5, 0.915}, 
    541345.0278164339}, {{5.5, 1.25, 0.915}, 
    493845.13464755006}, {{5.5, 2.0, 0.915}, 
    437127.722716284}, {{4.0, 0.5, 1.07}, 
    557993.383995382}, {{4.0, 1.25, 1.07}, 
    528343.954567613}, {{4.0, 2.0, 1.07}, 
    491109.63353426}, {{4.5, 0.5, 1.07}, 
    524463.329549847}, {{4.5, 1.25, 1.07}, 
    494319.026567362}, {{4.5, 2.0, 1.07}, 
    455866.45703618496}, {{5.0, 0.5, 1.07}, 
    500543.046316201}, {{5.0, 1.25, 1.07}, 
    470037.50360640406}, {{5.0, 2.0, 1.07}, 
    430649.63382796705}, {{5.5, 0.5, 1.07}, 
    483029.903555676}, {{5.5, 1.25, 1.07}, 
    452284.83559607796}, {{5.5, 2.0, 1.07}, 
    412284.058496425}, {{4.0, 0.5, 0.45}, 
    1222680.0}, {{4.5, 0.5, 0.45}, 1209000.0}, {{5.0, 0.5, 0.45}, 
    1200080.0}, {{5.0, 0.5, 0.605}, 803280.0}, {{5.5, 0.5, 0.45}, 
    1194050.0}};
Improve this question
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  • $\begingroup$ Related/duplicate: mathematica.stackexchange.com/questions/42999/…, mathematica.stackexchange.com/questions/69622/… $\endgroup$
    – Michael E2
    Commented Dec 11, 2021 at 0:23
  • 2
    $\begingroup$ The answers indicate that NMinimize can step outside the constraints, but eventually enforces them in the answer. When it steps outside the constraints, you get a InterpolatingFunction::dmval message. But if the answer is inside the constraints, then I doubt extrapolation has invalidated the answer. Is there some reason to doubt the result, other than the extrapolation warning? $\endgroup$
    – Michael E2
    Commented Dec 11, 2021 at 0:58
  • 2
    $\begingroup$ I don't know what "issue" you mean. The answers suggest the message comes from InterpolatingFunction when NMinimize steps outside the constraints. But the answer returned is within the constraints. So the step outside the constraint is rejected, and you can ignore the warning message. $\endgroup$
    – Michael E2
    Commented Dec 11, 2021 at 1:54
  • 1
    $\begingroup$ I meant that numerical optimization is accompanied by some uncertainty unless the function is convex. For instance, an optimizer might reliably return a local minimum, but if there are many local minima, it might not return the global minimum. A reason to doubt the result is that it is greater than one of the data points (I didn't check). Or in the case of experimental data, if the minimum value or its location seems inconsistent with the physical setup. I see no reason to think that the extrapolation caused NMinimize to fail to work properly. $\endgroup$
    – Michael E2
    Commented Dec 11, 2021 at 16:25
  • 1
    $\begingroup$ @MichaelE2 thank you very much for your input I appreciate it. Do you have any suggestions on how I might be able to best visualize the results of the optimization? I'm struggling to find a good graphing method that will include all data (ie both logdata1 values and data2 values) because I have 3D inputs (the same for both functions) with two outputs (one from each function). $\endgroup$
    – mathemagician617
    Commented Dec 11, 2021 at 16:28

1 Answer 1

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I thought, without checking, that the answer was probably sensible. The following confirms it and answers this comment:

Do you have any suggestions on how I might be able to best visualize the results of the optimization? I'm struggling to find a good graphing method that will include all data (ie both logdata1 values and data2 values) because I have 3D inputs (the same for both functions) with two outputs (one from each function). – mathemagician617 19 mins ago

f1 = Interpolation[logdata1, InterpolationOrder -> 3];
f2 = Interpolation[data2, InterpolationOrder -> 3];
(*point=Import["inits.txt","Table"];*)
{max, argmax} = NMaximize[{
   f2[x, y, z],
   f1[x, y, z] <= Log[0.5], 4 <= x <= 5.5, 0.5 <= y <= 2, 
   0.4 <= z <= 1.07},
  {x, y, z}
  (*,Method->{"Automatic","InitialPoints"->point}*)]
(*  {589924., {x -> 4.2973, y -> 1.43503, z -> 0.717169}}  *)
  
dp = DensityPlot3D[
   f2[x, y, z],
   {x, 4, 5.5`}, {y, 0.5`, 2}, {z, 0.4`, 1.07`},
   RegionFunction -> Function[{x, y, z}, f1[x, y, z] <= Log[0.5`]], 
   PlotLegends -> Automatic,
   AxesLabel -> Automatic
   ];

Show[
 dp,
 Graphics3D[{PointSize@Large, Red, Point[{x, y, z} /. argmax], 
   Arrow[{{x, y, z}, {x, y, z} + 
       0.25 Normalize[D[f2[x, y, z], {{x, y, z}}]]} /. argmax]}],
 ViewPoint -> {-2.5, -1.2, 1.2}
 ]

Show[
 RegionPlot3D[
  f1[x, y, z] <= Log[0.5`],
  {x, 4, 5.5`}, {y, 0.5`, 2}, {z, 0.4`, 1.07`}
  ],
 Graphics3D[{PointSize@Large, Red, Point[{x, y, z} /. argmax]}],
 ViewPoint -> {-1.3, -2.4, 1.2}
 ]

(Extrapolation warnings ignored.)

Confirmation that a local extrema has been found:

Log[0.5]
f1[x, y, z] /. argmax
(*
  -0.693147
  -0.693147
*)

D[f2[x, y, z], {{x, y, z}}] /. argmax // Normalize
D[f1[x, y, z], {{x, y, z}}] /. argmax // Normalize
(*
  {-0.126194, -0.299955, -0.94557}
  {-0.126194, -0.299955, -0.94557}
*)
Improve this answer
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  • 1
    $\begingroup$ If you want to see the gradient orthogonal to the surface, add BoxRatios -> Automatic to the Show[] command. $\endgroup$
    – Michael E2
    Commented Dec 11, 2021 at 17:11
  • $\begingroup$ Thank you very very much for your help. I greatly appreciate it. $\endgroup$
    – mathemagician617
    Commented Dec 11, 2021 at 17:25
  • $\begingroup$ @mathemagician617 You're welcome. $\endgroup$
    – Michael E2
    Commented Dec 11, 2021 at 17:39

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