merci monsieur

un vrai crack

Finally understood the weak duality theorem after endless YT videos

great playlist, concise and didactically valuable! thx :-))

Extremely well made video! It'd be nice if you make a tutorial on how to make those nice plots.

Amazing vedio, my English not very well, but still can get ur meaning quickly.

Interesting

Extremely good explanation thank you

Summarised my professors 3 hours class in 10 minutes! Thank you for such a simple explanation!

It's very clear. Very helpful!

please the code

🙏

so clear! thank you very much! you solved my problems!

90😅😊

和樂 自殺

Is this similar to lemke pivotal method?

In context what does pdf mean ?

Suscribed. Your channel is a gold mine. Concise and easy to follow. Thank you so much for your work.

Professor，I have a question. The reduced cost is based on the dN=ej. When computing the reduced cost regarding the basic variable, I think the dN=0. Does it still apply?

Thanks a lot ! I have a question that I hope you can answer. We have obtained the position where ℎ(𝑥∗)achieves its minimum value, that is, when 𝑥=𝑥∗ ,ℎ(𝑥) reaches its minimum. When I set 𝑥∗ = 𝑥𝑘 + 𝛼𝑑𝑘 to find the step size, is it possible for 𝛼 to fall outside the range[0,1]?

What an awesome lecture, the examples are very helpful!

vive la suisse !

Many thanks for taking the time to share your knowledge so articulately.

I would like to see a real example. In my case the line search begins from the current point in the opposite direction of the gradient. I must search along the line. I can just repeatably iterate along the line and evaluate the cost function until it no longer gets smaller. You are suggesting searching between two points but how far should the end point or 'low" point be from the current "high" point. You are assuming the boundary of the end point is known and will bracket the minimum point along the line search. What if it isn't?

Dear sir, Thank you for your series. I was doing some research in the field of trust region updates and found you video series. I have a question on this video, though. Let's say we are working in reals. By definition, a positive definite matrix is a SYMMETRIC matrix M \in R^N \times R^N s.t. x*Mx >0 \forall x \in R^N and ||x||>0. At 04:04 it is said, that the matrix D_k = inv(A + \tau I) and \tau \in R is such, that D_k is positive definite. Am I correct that such method will not work if A is not symmetric? Is there a remedy for that case? I know about using a diagonal or symmetrization of the matrix (M = 1/2(A+A*)). But are there any other good suggestions, apart from trust region type methods, especially if the Newton's method is applied to find a solution to the problem F(x) = 0, not the minimization problem? Thank you for your time in advance.

kroasan

Thank you Sir

I appreciate you deeply due to your amazing teaching.

great

Wooooooooooooooooooooow. Dear Professor, your explanations are brilliant and helpful. I also downloaded your book. Thank you so much for your services.

you saved my life!

Extremely concise and lucid explanation. Thank you Prof. Michel.

Dear Prof, is there any reason we set the upper bound for the nest parameter as 10?

love the french accent. cheers !

Don't understand customer behaviour. Customers dont go to a bar when it is closed. And i guess just leave when service takes too long.

Cristal clear

Vous êtes une référence à mes yeux ! Je connais votre chaîne depuis la fac et désormais je travaille dans un projet autour de la MDA / MDO et vos vidéos sont d’une grande aide, merci !

Dear Michel, Thank you so much for this insightful video. Can you make a video demonstrating choice modeling with latent variables in SPSS, Stata or other softwares? That would help us figure out how to integrate SEM with discrete choice modeling in the form of Structural choice modeling. Thank you so much again

The video Survival of the fittest is not available on KZbin anymore.

Well...math is not the strongest area of the French:). Wines and good food maybe.

Dr. Bierlaire you are the best. I have been involved with SP studies for the past 34 years from Park and Ride , LRT , BRT , early or late start time ( peak spreading) , risk averse propensity at signalised junctions. having conducted over 30K SP surveys myself on the past 34 years I always have had questions and never found transparent answers concerning theory and estimation but you are a super start who explains leaving nothing un-answered. A Big Thanks

Merci beaucoup !

thank you for the tutorial!

very good and intuitive explanation. Thank you so much sir! you make my learning a really wonderful experience

Thankyou.

Where is the part 3?

Thank you so much. So well explained, exactly what I needed!

Why I could not install biogeme through anaconda?

Monsieur a 2:38 nous avions les couts reduits de c6 = -1.25 et celui de c5 = -0.75, c'etait pas plus interessant de choisir c6 comme variable entrante vu qu'elle diminuera la fonction objectif plus que c5 ? Merci.

Hey please at 3:27 i didn't understand why N * dn is the same thing as the SUM of (Aj * dj) with j from m + 1 to n