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Computational Linear Algebra

fast.ai

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Re: Computational Linear Algebra

#2

Post by rabreu08 »

Looks like a good course. I think it would benefit if they added some module on implementing some basic Linear system of equations solvers, like gradient or steepest descent. Or even GMRES/MINRES or so.. The amout of knowledge that i gained from trying to implement these was remarkable.

Re: Computational Linear Algebra

#3

Post by jph00 »

rabreu08 wrote: post #2

Looks like a good course. I think it would benefit if they added some module on implementing some basic Linear system of equations solvers, like gradient or steepest descent. Or even GMRES/MINRES or so.. The amout of knowledge that i gained from trying to implement these was remarkable.

Gradient descent is covered. We even show a pytorch implementation :)

Re: Computational Linear Algebra

#4

Post by rabreu08 »

jph00 wrote: post #3
rabreu08 wrote: post #2

Looks like a good course. I think it would benefit if they added some module on implementing some basic Linear system of equations solvers, like gradient or steepest descent. Or even GMRES/MINRES or so.. The amout of knowledge that i gained from trying to implement these was remarkable.

Gradient descent is covered. We even show a pytorch implementation :)

Great! It's 2AM here and i totally missed it.

I will definitely give this course a try and spread the word. Thanks

Re: Computational Linear Algebra

#7

Post by fdej »

> Locality: traditional runtime computations focus on Big O, the number of operations computed. However, for modern computing, moving data around in memory can be very time-consuming

I need to nitpick here... Big O notation is a way to describe growth rates of functions. You can count data movements (or anything else) with Big O.

Re: Computational Linear Algebra

#8

Post by vog »

fdej wrote: post #7

> Locality: traditional runtime computations focus on Big O, the number of operations computed. However, for modern computing, moving data around in memory can be very time-consuming I need to nitpick here... Big O notation is a way to describe growth rates of functions. You can count data movements (or anything else) with Big O.

Moreover, "moving data around in memory can be very time-consuming" means that in the end, it is still about time, not about memory.

So the correct way would be to translate memory access to time, but that means modelling the memory hierarchy, modelling caches in general, and finally perhaps modelling the specifically used caching strategies.

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