Applied Optimization - Steepest Descent

Steepest descent is a simple, robust minimization algorithm for multi-variable problems. I show you how the method works and then run a sample calculation in Mathcad so you can see the intermediate results.

[gerardoav]

Wow!

I was never good in Maths. I’m a physician, and quite old, 55 years of age. And I could understand everything.

Oh, and yes: it’s the COVID-19 lockdown motivation...

Thank you so much. You can explain anything, I bet!

[venumadhavrallapalli]

guess a starting point, look in thesearch direction(jacobian vector), and search along a 1D variable, repeat. Very clear explaination, thank you.

[KeesJansma]

I didn't understand a thing in my textbook, but this is really clear! Thank you sir

[omercix]

I love you, finially finished my assignment with your help :)

[robertgawlik]

I'm so glad I found this video. Thank you very much.

[sanjayksau]

Beautiful explanation. Is there any video using conjugate direction as well?

[rmttbj]

24:21. Could you provide some guidance (a link to an example would also be fine) as to how to reach d = 0.179 if we are to calculate the value manually? Thank you very much :)

[krishnadas]

That was beautiful. Thank you very much.

[paolaalvarado]

Thank you so much, your explanation was very clear!!!

[beyzabutun]

This was very helpful. Thank you so much!

[ZinzinsIA]

Very interesting but many things I don't understand. Often when considering the gradient, we consider it at a particular point.

When we draw the gradient vector, it's a vector in the same space and coordinate system, but originating from the point where we calculated the gradient, not a vector coming from the origin of the coordinates space ?
Then, If I understand gradient like derivative is a slope giving the direction of biggest change, I don't get the intuition on why the gradient lying on this slope is oriented towards the direction of steepest ascent. Does it have anything to do with basis orientation/direction ?
I mean when we draw a slope and say it's the slope of the derivative at a particular point, it does not tell us if it is going up or down. I mean rate of change could be toward the decreasing side of the slope, so why do we say gradient always point towards steepest ascent

[DouglasHPlumb]

That "d" is what brought me here - so what are the methods to find it other than getting another single dim minimization problem?

[velagasohith]

What is basic difference between steepest descent method and Marquardt method

[rowdyghatkar]

The video was great... But what year is this... Did anyone else get some 90's vibes😀...

[BSplitt]

For these videos, can you please disable the clock in the background?

[brandonrobertson]

"Boats Boats Boats" - Laura Bell Bundy

[az]

lol sorry to bring this up but it sounded like you said the n word in 10:53


Great tutorial though. much appreciated

[Krautmaster]

i suggest to put a mic on ur tshirt =)