thank you for your course. Its very helpful for me. How can I get the matlab m file in this video?

the beauty of this is underappreciated

Thank you, Sir, for the video. However, I have a question to ask. I have a pre-trained TensorFlow model for making predictions and I would like to use it to apply Model Predictive Control (MPC) using CasADi. I read that only PyTorch models can be used with CasADi. Is there another way to use a TensorFlow model with CasADi without converting it to PyTorch?

Thank you for sharing. However, I have a question, please. I am currently implementing an MPC to control the temperature inside a room. To model the system, I used a neural network that takes as input a window of data (disturbance_w, control_w, output_w) to predict the output over a prediction horizon. Then, I use these predictions to calculate an objective function ( j = [(r(k+1)-y(k+1)).T*a*(r(k+1)-y(k+1) + delta_u.T*b*delta_u)] in order to obtain the first command to apply to my system to get the first output. For this, I use CasADi, but the control proposed by this library remains constant regardless of the output values (the output does not follow the reference). Do you have any advice to improve this?

when is casadi 3.6.6 being released?

Hey there I'm excited to use this update. Do you have this code in Matlab as well? I'm trying to follow along by coding it myself. Thanks!

This is really great. Can i try fatrop for MHE ? Also, I wish your webcam video was showing on the bottom right. Part of the code got covered.

Nice demonstration. I wish there was a feature within opti that can rearrange variables to obtain the best sparse structure. Compared to hpmpc, it can be expected to more robust to initial guesses which exploiting the structure as hpipm?

amazing

How would one implement the function at 1:35 in Python a way that allows the function to take x and u as two different vectors? When I try to convert this to Python, I get the error: TypeError: unhashable type: 'list' Code is: x1 = SX.sym('x1') x2 = SX.sym('x2') u = SX.sym('u') x = [x1, x2] ff = [((1-x2**2)*x1 - x2 + u), x1] f = Function('f',{x,u},ff)

What are the p1 & p2 here? Are they f1 & f2? Or something else?

Hi, could casadi write constraint equations for optimization problems with the ability to call nonlinear external functions like matlab's fmincon? Because I need to call an external library when writing constraints.

casadi graph computation is amazing.

Dear Joris, thank you so much for this great tutorial, would you please provide the source code for the tutorial? The link is just loading and giving nothing!

How does the AD capabilities of casadi compare to JAX?

Incredible tool and great presentation of it's capabilities

Thank you very much for the presentation. Is it also possible for Casadi to solve Partial Differential equations (and optimization involving PDE)? Thanks!

maybe making some tutorials will help others using casadi

Since it's a pretty old video, I doubt I will get an answer, but shouldn't we define the first state as x(:,0) instead of x(:,1) ?

Can we use dual numbers for integration?

you have unnecessarily made it difficult, it is easy although....

Thank you very much!

That smooth fade in at start

amazing, Thank you!!!

We verwachten veel films in het Nederlands

amazing stuff，when will release new version

Thank you! It is so exciting to see such a tutorial!

You should have given an example with variables defined; this made it more complex. What's the p1 and p2 arguments all about

Great video! Thanks :)

Thank you so much for this video.

Dear Sir. I am having problem in MHE (Moving Horizon Estimation) method to use with CaSADi in Matlab? Could you see my problem. May i have your email address that you use frequently.

Great explanation, thanks!

I think the presentation is great but you look a little bit nervous, :D,

I always found it easier to work always on the tree and then just work it with graphs algorithms.

Hi Joris, please would you kindly post the video recording of the casadi course. I am new to Casadi and not able to attend the course happening in March. The videos on mathematical background of Casadi would be highly appreciated.

What if the initial value starts outside the constraint for an MPC application?

Thanks for the clear explanation!

Hi, how it could apply Casadi for MPC problems?

Hi Joris thanks for sharing this. I'm a beginner with CasADi and didn't know about some of these features - being able to generate code from an opti stack formulation is really great :)

just to check my undertsanding - are you writing += (i.e. take the previous value and increment) or plus-minus in the second half of the video (\pm in LaTeX)? I presume it's the first, but your notation is a little hard to track. Maybe a plus with a double underline would be clearer? Otherwise lovely video! Look forward to seeing more

at 6:17, why is (partial f4)/(partial P1) not partial (f4/partial P2)?

Thanks for the video. Could you explain how I to call casadi from Julia or matla please. Thanks so much

Have a wonderful 2019 Joris , cheer from Vietnam !

Setting aside the interesting topic, I was amazed by your writing things from 'behind' some class wall and it not appearing mirror-imaged :)

can you show how the Dulmage-Mendelsohn can be implemented and run? the toturials on the website seems not working for me. Thank you

it's possible use sympy - to do some heavy lifting - might be worthwhile to do a video on this >>> from sympy import sin, cos, Matrix >>> from sympy.abc import rho, phi >>> X = Matrix([rho*cos(phi), rho*sin(phi), rho**2]) >>> Y = Matrix([rho, phi]) >>> X.jacobian(Y)

Focusing on Jacobians while trying to convey the essence of AD is a non-matter especially if this is a high level view. This does not explain the essence of AD

really excited to see CasADi work for my project! Thanks!

Hi Joris, How to get the user guide for CasADi v2.1.0?

Thank you, Can you explain how to use OPENAD/F please