About USL Scalability Modeling with Three Parameters

[Sky89]

Hello..

Read this:


About USL Scalability Modeling with Three Parameters

As you have noticed i have implemented Universal Scalability Law for
Delphi and FreePascal, and i have also included the executable programs
inside the zip file, you can download it from:

https://sites.google.com/site/scalable68/universal-scalability-law-for-delphi-and-freepascal

But my above USL program is using two parameters , alpha and beta.

Now i am actually implementing the USL with three parameters, read
about it here:

http://perfdynamics.blogspot.com/2018/05/usl-scalability-modeling-with-three.html


So here is the USL function with three parameters that i am passing to
the following partial derivatives calculator:

https://www.emathhelp.net/calculators/calculus-3/partial-derivative-calculator/?f=%28x%29*k+%2F%281+%2B+%28n*+%28x+-1%29%29%2B+%28m+*+x+*+%28x+-1%29%29%29&var=k&steps=on


Here is the function: (x)*k /(1 + (n* (x -1))+ (m * x * (x -1)))


And here is the partial derivatives that you have to pass
the nonlinear regression solver:

And you have to solve with the order of integration that is k

And you have to solve with the order of integration that is n

And you have to solve with the order of integration that is m


After that you will get your partial derivatives functions that i will
pass to my nonlinear regression solver, so i am actually upgrading
my USL for Delphi and FreePascal to support USL with the three Parameters

So stay tuned ! i will soon bring you the updated version of my
USL that will use three parameters for Windows and Linux and that will
contain the executable programs for Window and for Linux.



Thank you,
Amine Moulay Ramdane.

[Sky89]

Hello..

Read this:

I correct a typo:

Here is the USL function with three parameters that you have to solve
with the partial derivatives calculator:

https://www.emathhelp.net/calculators/calculus-3/partial-derivative-calculator/?f=%28x*k%29+%2F%281+%2B+%28n*+%28x+-1%29%29%2B+%28m+*+x+*+%28x+-1%29%29%29&var=k&steps=on


Thank you.
Amine Moulay Ramdane.