## performance optimization – Why does NonlinearModelFit repeat assessments of identical functions?

If you look at how `NonlinearModelFit` you find that the model is evaluated with the same parameters over and over again. I have a very expensive model so it wastes a lot of time.

Here is the example of version 12.1 help.

``````data = {{6.47, 3.65}, {7.43,
3.45}, {3.9, -2.94}, {4.8, -1.29}, {2.48, -0.35}, {6.32,
3.16}, {2.59, -1.19}, {9.13, -2.}, {3.81, -3.04}, {3.33, -2.68}};

ClearAll(model);
model(a_?NumberQ, b_?NumberQ, c_?NumberQ) :=  Module({y, x},
Sow({a, b, c});
First(y /.
NDSolve({y''(x) + a y(x) == 0, y(0) == b, y'(0) == c},
y, {x, 0, 10})))

t1 = Timing(
r1 = Reap(
nlm1 = NonlinearModelFit(data, model(a, b, c)(x), {a, b, c}, x,

(* {4.375, Null}  *)
``````

I added a `Sow` and `Reap` so we can find the number of ratings. Looking at the number of terms that have been collected.

``````L1 = Length(r1((2, 1)))
``````

(* 6280 *)

If we look at the first 100 values, they all seem to be the same. However, they could be almost similar, so I eliminate the identical terms using `Union`

``````Union(r1((2, 1, 1 ;; 100))) // InputForm

(* {{1., 1., 1.}, {1., 1., 1.0000000149011612},
{1., 1.0000000149011612, 1.}, {1.0000000149011612, 1., 1.}} *)
``````

This may require further investigation as we are examining small differences, but it appears that only 4 assessments were different. If there is a repeated assessment, memorization seems to be a good idea. I implement this as follows:

``````ClearAll(model);
model(a_?NumberQ, b_?NumberQ, c_?NumberQ) :=
model(a, b, c) =  Module({y, x},
Sow({a, b, c});
First(y /.
NDSolve({y''(x) + a y(x) == 0, y(0) == b, y'(0) == c},
y, {x, 0, 10})))

t2 = Timing(
r2 = Reap(
nlm2 = NonlinearModelFit(data, model(a, b, c)(x), {a, b, c}, x,

(* {0.1875, Null} *)
``````

The number of job evaluations is now

``````L2 = Length(r2((2, 1)))
``````

(* 227 *)

Thus, a much lower number of function evaluations and the calculation is performed much faster. The time and function evaluation report is

``````{t1((1))/t2((1)), L1/L2 // N}
``````

(* {23.3333, 27.6652} *)

Itâ€™s an acceleration of 20 times. So it seems that `NonlinearModelFit` makes unnecessary function assessments and that memorization makes a huge difference.

My questions are:

1. Did I miss something?

2. Why `NonlinearModelFit` do unnecessary job evaluations?

Thank you

## Difficulty finding a plug assessments / average numbers

The value is not predefined. It is essentially a numeric box with no defined value, but it covers the range 00001 to 99999.

The user sees the image and enters his personal evaluation … For example, "I think it's worth \$ 100", he enters 100. This value of \$ 100 is then displayed next to the 39; image in the form of "evaluation".

The next user arrives, repeats the process and assigns a value of \$ 90 to enter "90". Automatically, the value of updates from 100 USD to 95 USD (average of the number of evaluations, 100 + 90/2 = 95)

If this does not match the available Badge / Reward plugins, how much would php code cost? Just a rough estimate because I really hoped that a plugin already exists.

Thank you so much!

Angus

## How can I run multiple different kernels for multiple assessments of multiple scripts means Linux terminal?

I have several scripts to run, with the command: math.9 <script_name.m, on a Linux server based on a Linux terminal. The version of Mathematica on the server is Mathematica 9. I know that I can not run more than 7 processes per kernel. Is there a way to start another kernel terminal command or should I use Parallelize in the script? (Even if I do it this way, I can not start Mathematica because it indicates that I have reached the maximum number of processes).

Thanks for the help

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.