Computational Chemistry 3.4 - Conjugate Gradient

Short lecture on the conjugate gradient energy minimization algorithm.
Conjugate gradient is a more advanced algorithm than steepest descent for obtaining a minimum energy configuration of a molecular system. The step history is used to accelerate the convergence to a stationary point, improving the direction of the displacement relative to steepest descent.
Notes Slide: i.imgur.com/yZS...
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[@l0703994]

I don't think you are explaining the conjugate gradient method at all, especially when you are talking about taking weighted average (around 2:45) which is not really essential to conjugate gradient

[@nkhullar1]

It is interesting I am a computer science majoring in Machine Learning and fields are criss crossing . I completely understood what you are talking. Thanks.

[@TMPChem]

Completely agreed. When I first started deep diving into the math of what machine learning actually does, I was like, "Hey, this is just linear algebra and multidimensional function minimization. I know how to do that. I did that all day for years."

[@pankajadhikary2091]

Awasome video sir, Sir what kind of software you are using for writing and presentation?

[@Kichaka_Ranch]

This is so helpful. Please keep up

[@TMPChem]

Thanks Michael. Hoping to have another chapter or two released around the end of the year.

[@arturorenteria4943]

Python SciPy library uses different optimization methods, do you recommend any specific one?