☀️_☀️☀️☀️☀️☀️☀️☀️☀️

you are th beeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeest😭😭💥💥❤❤❤❤💯💯💯

Amazing videos, Clear and Concise! Thank you for covering this topic in such a brilliant way!

Very good, finally understand how that best fit line we get!!

God, I know this sounds silly but I didn't know that the plane was the equivalent to the line. Now I comprehend this as I should've since the beginning. THANK YOU AlphaOpt

Superb!

drink some water man

clear and brief idea

Thanks a lot, I have the curse of being a visual learner and this was amazing.

Woah whole meat

Thanks!

This is close to application math, this is so good for gave us the biggest picture of it!

Visual learning makes things so much better.

slope is delta Y divided by delta X

Thank you, this was a great introduction to tge topic.

Great video and helpful channel! Khan academy and the organic chemistry guy are getting old and less helpful as school curriculums develop. Super grateful for these simple, direct explanations

teacher I developed a heuristic and would like to share it. My heuristic uses topology and concentric circles. What do you think?.

more videos please

in 2 mins just you explained everything

How to solve this when the x and y are bounded?

Oh, it's really exciting, i hope to see more videos from you! So far i've seen some of IBM Technology's videos on it, but i'd like to know what the current knowledge and technology of AI is as of september 2023... I wish they'd post longer than 5-8 minute videos on these subjects... It is not that they are not informative, just that they are never in-depth. I remember MIT OpenCourseWare courses were hours upon hours, but nowadays, you do searches about AI, Machine Learning, NLP, Data Analysis, and they're all very general, like an overview at best, it is good for me as a beginner, and i do not promise that i would understand the more extended videos, but i am so curious and intrigued by them, i could easily spend hours learning about it, if only the content was there.

Simple yet extremely informative👍

Just perfect. Thanks

lovely brooo, such good animation, now i have the concept in my head.

This is a great little video !

So clear

how the hell is this O(n!) ??

Thank you, what is the name of the algoodo tolbox you used for simulated annealing?

compact and thorough at the same time. thanks !

Thank you... ❤

Imagine being a Salesman and this actually happens (I k it can happen irl on godddd it's a joke)

Explain the christofian 1.5 solution and give an heuristic example as well please

excellent explanation.Thank you so mcuh

But residual != error?

Works fine in R2021b. % set initial guess values for box dimensions lengthGuess = 1; widthGuess = 1; heightGuess = 1; % load guess values into array x0 = [lengthGuess widthGuess heightGuess]; % call solver to minimize the objective function given the constraint xopt = fmincon(@objective,x0,[],[],[],[],[],[],@constraint,[]) % retrieve optimized box sizing and volume volumeOpt = calcVolume(xopt) % calculate surface area with optimized values just to double check surfaceAreaOpt = calcSurface(xopt) % define function to calculate volume of box function volume = calcVolume(x) length = x(1); width = x(2); height = x(3); volume = length * width * height; end % define function to calculate surface area of box function surfaceArea = calcSurface(x) length = x(1); width = x(2); height = x(3); surfaceArea = 2*length*width + 2*length*height + 2*height*width; end % define objective function for optimization function obj = objective(x) obj = -calcVolume(x); end % define constraint for optimization function [c, ceq] = constraint(x) c = calcSurface(x) - 10; ceq = []; end

Excellent! Thank you.

great explanation!

Omg, your explanation is better than other youtube videos and my teacher because I'm a visual learner.

Why use the squares instead of the absolute values?

The Nelder-Mead method is not explained long enough to understand it.

Thank you for simplifying this

<3

<3

no example, pretty useless

Good explanation 👍

Excellent video and also quite easy to understand

This is a very similar problem I have but I have a linear obj func and constraint, and constraint is an equality. When using SLSQP I get error "singular matrix c in lsq subproblem". Seems I should use linprog but I'm not sure how or whether this type of problem can be converted to linprog. Any ideas?

I suppose we can also say optimization is choosing the best input or best process, or both the best process and best input to yield the best output

Have you tried slime mold?

Nice 👍🏽