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FaridMoussaoui
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Re: 100 millions time faster than ODE methods

December 3rd, 2019, 9:05 pm

In December 2015, Google announced that the D-Wave 2X outperforms both simulated annealing and Quantum Monte Carlo by up to a factor of 100,000,000 on a set of hard optimization problems.[30]
Are they still in the proof of concept paradigm?
 
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Cuchulainn
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Re: 100 millions time faster than ODE methods

December 4th, 2019, 12:06 pm

In December 2015, Google announced that the D-Wave 2X outperforms both simulated annealing and Quantum Monte Carlo by up to a factor of 100,000,000 on a set of hard optimization problems.[30]
Are they still in the proof of concept paradigm?
I only discovered it via trackstar few weekends ago.

Managed to download (you need to register) + they give you a dashboard + 0.01 secs per month free. 
That dampened by initial enthusiasm somewhat. 
When will it be a commodity?
Step over the gap, not into it. Watch the space between platform and train.
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Cuchulainn
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Re: 100 millions time faster than ODE methods

December 22nd, 2019, 3:02 pm

There are many ways to measure time.

www.youtube.com/watch?v=0QVPUIRGthI
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Cuchulainn
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Re: 100 millions time faster than ODE methods

September 21st, 2020, 5:57 pm

Image


This is a trailer from a recent very good thesis on Heston and Rough Heston. Instead of ANN being [$]10^4[$] faster, in this case it is [$][8,17][$] times slower.

Thesis public domain soon..
Step over the gap, not into it. Watch the space between platform and train.
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tagoma
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Re: 100 millions time faster than ODE methods

September 21st, 2020, 10:20 pm

Image


This is a trailer from a recent very good thesis on Heston and Rough Heston. Instead of ANN being [$]10^4[$] faster, in this case it is [$][8,17][$] times slower.

Thesis public domain soon..
Was the very purpose of this research to compare the speed of "traditional" methods vs ANN? What methodologies were applied to prevent biases? And isn't it apples vs bananas, anyways?
 
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Cuchulainn
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Re: 100 millions time faster than ODE methods

September 22nd, 2020, 8:18 am

No, there are several goals. Just be patient before jumping to conclusions. 

A by-product is to debunk the myth than NN is 'fast'. In that sense the above snippet is relevant to the current thread.

//
I also discuss the software aspects in my recent Thalesians video. And the speech by Grady Booch is a gem ("Software design with AI components").
Step over the gap, not into it. Watch the space between platform and train.
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Cuchulainn
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Re: 100 millions time faster than ODE methods

September 22nd, 2020, 4:09 pm

"What methodologies were applied to prevent biases?
1.  5-fold cross validation. [$]R^2 = 0.999[$]

2.  Other works take a more laissez-faire approach.

I have examined their code, which yield extremely high accuracy. However, they are evaluating the accuracy using in-sample data, which tells us nothing about the accuracy of the ANN on unseen/out-of-sample data." 

Sneaky.
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tagoma
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Re: 100 millions time faster than ODE methods

September 22nd, 2020, 7:42 pm

Thank you for your comments Cuch.
I long to have a look at this piece of research.
Sincerely,
tag
 
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Cuchulainn
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Re: 100 millions time faster than ODE methods

September 22nd, 2020, 8:45 pm

Thank you for your comments Cuch.
I long to have a look at this piece of research.
Sincerely,
tag
tag,
I reckon 2-3 weeks and then it will hopefully be public domain :-)
Step over the gap, not into it. Watch the space between platform and train.
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JohnLeM
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Re: 100 millions time faster than ODE methods

September 23rd, 2020, 9:12 pm

Image


This is a trailer from a recent very good thesis on Heston and Rough Heston. Instead of ANN being [$]10^4[$] faster, in this case it is [$][8,17][$] times slower.

Thesis public domain soon..
That is more or less what I was expecting. I guess the thesis used a customized finite difference 2D scheme ?
 
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Cuchulainn
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Re: 100 millions time faster than ODE methods

September 24th, 2020, 6:51 am

Image


This is a trailer from a recent very good thesis on Heston and Rough Heston. Instead of ANN being [$]10^4[$] faster, in this case it is [$][8,17][$] times slower.

Thesis public domain soon..
That is more or less what I was expecting. I guess the thesis used a customized finite difference 2D scheme ?
Kind of: it is a rough Heston model that was solved as a fractional Riccati equation. It also uses the characteristic function and FFT.
Step over the gap, not into it. Watch the space between platform and train.
http://www.datasimfinancial.com
http://www.datasim.nl
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