Python Tutorial: Learn Scipy - Optimization (scipy.optimize) in 13 Minutes

The scipy.optimize package provides modules:
1. Unconstrained and constrained minimization
2. Global optimization routine
3. Least-squares minimization and curve fitting algorithms
4. Scalar univariate functions minimizers and root finder
5. Multivariate equation system solvers (root)
In this video, unconstrained and constrained minimization will be discussed.
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[@LilianBoulard]

Hi I didn't get why in the inequality_constraint() function, you return "x1**2 + x2 - 50" when the problem statement is "x1^3 + x1 * x2 >= 50". It should be "x1**3 + x1 * x2 - 50", right ? Is it a mistake or a simplification I don't understand ?

[@eMasterClassAcademy]

Thanks for pointing out, it's a typo. My bad.

[@ehtishamasghar6222]

How the optimizer knows that inequality is greater than or lesser than?

[@eMasterClassAcademy]

Thanks for your questions, it's by default. Greater than is the default mode.

[@ehtishamasghar6222]

@@eMasterClassAcademy thank you for replying

[@ssjvegeto4ever]

Great video, helped me a lot! Do you know by any chance how to do such an optimization with vectors, too? I tried to define an objective function by defining my variables as vectors - but that didn't work out. Thanks a lot in advance!

[@VictorWaknin]

This was of great help! Thank you so much for the succint explanation.

[@sverma333]

very clear, thank you

[@lowerlowerhk]

This is much more understandable for beginners than the Scipy manual!

[@sciencewithcats2274]

Love the short version at the end! So much more understandable and clear!!!

[@eMasterClassAcademy]

Awesome, thank you!

[@swadhinbanerjee2866]

How to store output value of each variable

[@men6829]

thank you very much can you use also the jac option with an objective function that calculate the MAE ??

[@ashishk81]

how to add non negative constraint , for example all x[0], x[1], x[2]...x[10] should always take positive value ..please explain .

[@antianti4331]

What's the need for second constrain? If equation is equal to 100, it's obviously greater than 50.

[@eMasterClassAcademy]

Thanks for watching. The code is correct, but it is a typo in the picture. Equation 2 should be X1**2 + X2 - 50 Hope it helps

[@depokz]

If you had lots of variables i.e. x = [1, 1, .... 1, 1], how would you define a constraint/bound so that each element is greater than or equal to the prior element? i.e. [1, 1.4, 1.4, ... 3, 5]

[@JohnChu-z4w]

watched 2023.9.9

[@anthonyedwardstark8471]

great. continue doing this

[@noname-sr3em]

hello sir, what if I have 2 inequality functions for this example? how sould i add second inequality function and if they are less than function how the form should be? thanks in advance :)

[@eMasterClassAcademy]

Sorry about that I am not sure if the optimize function allows to have more than one objective function. For the "less than", should it be simply adding "minus" in the front? Quite not able to help. Thanks for your question.

[@mrhamfi9489]

Simply create a function for each inequality Let's say function1 , function2 Type: con1={"type":"ineq","fun":function1} con2={"type":"ineq","fun":function2} cons = [con2,con2] When calling minimize , put constraints=cons Voilà

[@akshaysunil2852]

what is lambda function

[@sandeepmandrawadkar9133]

Great Explanation

[@freemisfitgumbo9607]

does it matter which visual studio code i use

[@noname-sr3em]

yes it does matter, you can learn explanation by reading their below description. e.g. some of them are using for web design , some of them are using for general usage etc..

[@isaacadjetey9618]

May I know the IDE you used please?

[@Rejich89]

How can you do it for a maximization problem?

[@johnchoi3840]

minimize (-1*function )

[@djhak9]

hi, how to solve non-linear equation with two unknown variables and two variables with list[] something like this i.stack.imgur.com/2ZOfu.png

[@eMasterClassAcademy]

1) Setup the objective function - what do you want to minimize? 2) Setup the constraints - what are the equality constraints or inequality constraints? 3) Define the function and constraints as shown in the video 4) Run the minimize function (Scipy) In the picture that you show, you might consider setting x1 as your mean and x2 as your std. But it's quite a bit confusing about the constants in the picture. You might want to expand the summations and think about how to define your objective function.

[@daniel.dearaujo]

Thank you!

[@bayususanto7389]

good job! thank you for sharing

[@boymongto]

Sorry I want to ask about the initial value of x0 = [1, 1] (line 29) . Does it has to satisfy the equality constraint and inequality constraint ?

[@tamerelsayed6368]

no

[@jomurciap]

Thanks

[@lilacfleur5439]

Sorry, what to do if I need to apply boundaries only for 2 parametrs among 4?

[@mrhamfi9489]

Let's say we have a,b,c,d our variables and want to set only a,c,d boundaries: btw 1,6 Simply : b1=(1,6) b2=(None,None) Bounds=(b1,B2,b1,b1)

[@Bilal-qb3kw]

Hello guys ! I have the same optimization problem, where a solve a constrainted function with SLSQQ method. My question is: Does anyone know how can I visualize (e.g. with matplotlib) the progress of the optimization, I would like to visualize the progress so that we could see visually how does the algorithm comes to the result (here the result is the x: array ([1.99999, 46.0000]). It would be great to see some smart people who can help me :_) BR

[@mongcm4508]

good

[@tigrayrimey6418]

Great lecture!

[@freemisfitgumbo9607]

when i run result i get this error - (Unable to import 'numpy'pylint(import-error)) please help mybe through another video or just pure intruction

[@jia_nanwang3130]

if I have three sets of parameters (x,y,z), do I need to convert them into 1-dimensional form?

[@dennywey9816]

Yes, perhaps you have to

[@jia_nanwang3130]

@@dennywey9816thanks，and if there has an optimization function same as minimize?

[@dennywey9816]

@@jia_nanwang3130 ah.. what do you mean by "same as minimize"? A function that accepts 3 dimensional input?

[@Eminosrrr]

Thank you so much. I can optimize things in Chinese now.

[@maximilian5862]

macher!