thanks

Thank you for this! Where can one find the book(lectures) you use for the explanation?

Dividing the learning rate by two works better. However I divide the learning rate by two until it is very small and will then take the step anyway. If I make a successful step then I multiply the learning rate by 4 not to exceed the original learning rate. This way the learning rate adapts to the terrain which rarely like a bowl in reality.

Thank you. where can i get more video on algebra cones

x es semipositive in the convex cone section

Great explanation! Using a 2D explanation makes it so much easier to visualise and understand! Thanks!

Thank you so much, I now have a clear geometric intuition in my head!

Nice explanation!

God i hate this type of math the most. Why must I learn this why lord why.

Many thanks for your clear explanation. Just a minor correction: Almost at the end of the lecture, it should be noted that the column space of the transpose of matrix M is orthogonal to the null space of matrix M.

i was loong for dichotomous search for c language but this shit is good too

Oh thank you from Vietnam !

Thanks for your video. I think this series is being underestimated since you may quickly understand what the algorithm does without any prior knowledge like Krylov space.

Great video. Thank you

I have a doubt. It is mentioned in the video that for 2 constraints, there are four things to check. I understood that we are looking for the solution on the boundaries of the contraint set where at least one of the constraints are active. But the optimum solution can be a point in the interior of the constraint set where no constraints are active. How will I get the internal optimum point with unconstraint optimization technique?

Its been months searching for it please send me a matlab code for this

I need a matlab code of that please

How much iterations it will take, I mean is there fixed number of iteration like conjugate gradient.

Hello dear prof. Mitchell. I went through this lecture and noticed that constraint minimiser x* = (1, 2) is the solution closest to the unconstraint minimiser x*_uncontraint =(0,5). Is that a coincidence or is there some rule of thumb in this?

Awesome video. Thank you!

Cool lecture! May I ask what's the reference text are you using? Thanks and keep it up

Me puedes compartir la presentación???

hi, may i know what 'k mod n' means and n stands for what?

Superb, thanks for the clarity

Thanks!!

Could you explain why y is in R(M^T) I thought the R(M^T) is the same as N(M) = S