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Adjoint Algorithmic Differentiation Masterclass (description)

"Adjoint Algorithmic Differentiation Masterclass"

Category:Business
Address:9-13 Bloomsbury street London, , United Kingdom WC1B 3QD
Phone:+44 (0) 207 316 9970
Event dates:3/8/2017 - 3/8/2017 (This event has already taken place)
Event web site:http://go.evvnt.com/82096-1
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Speakers/Presenters:

Topics to be Covered:

  • Quantitative Finance
Contact:stephen.body@incisivemedia.com
Organizer phone:+44 (0) 207 316 9970
Offering type:Data not provided
Specialty:Data not provided
Event frequency:Never
First Meeting:
Total events:
Est. attendance:
Target audience:Any
Other target audiences:
Host Sponsor:Risk Training
Est. # of exhibitors:
Currency:British Pounds
Language:English
CE Credits:
Organizer URL

Description:

In computational finance it is very important to be able to compute price sensitivities or `the Greeks' for hedging and model calibration. Adjoint methods are a well-established mathematical approach for efficiently computing sensitivities when there are multiple input parameters, but only one output quantity. In this case, the computational cost is similar to the original pricing calculation, whereas the standard linear sensitivity approach would have a cost proportional to the number of inputs.

In this one-day course, we will discuss the mathematical foundations for adjoints methods, algorithmic differentiation (AD) as a general computational technique for the efficient calculation of price sensitivities, and the use of AD software as a way to generate the adjoint code. We concentrate on its application to Monte Carlo methods for SDEs, but also cover finite difference methods for PDEs. Calibration is considered as a target application which can benefit tremendously from the use of adjoint AD (AAD).

Practical examples/exercises will be based on techniques for hand-coding adjoint implementations and the AD software tool dco (derivative code by overloading) for C/C++. We discuss the implementation of check-pointing methods for handling the often prohibitive memory requirement of adjoint code. The specific structure of ensembles (found in Monte Carlo methods for SDEs) and evolutions (found in finite difference methods for PDEs) needs to be exploited. More generally, adjoint code design patterns are proposed to obtain efficient, robust, and scalable adjoint code.

Schedule:

9:00 am - 5:00 pm

Registration Fee Details:

Standard Pricing: GBP 799

Other items that tuition includes:

Travel and hotel arrangements:

 
 
Contact Person:Stephen Body
Contact Emailstephen.body@incisivemedia.com
Organizer Address 1Haymarket House, 28-29 Haymarket
Organizer CityLondon
Organizer State/Province: (Outside US)
Organizer Zip or Postal CodeSW1Y 4RX
Organizer CountryUnited Kingdom
Organizer Phone+44 (0) 207 316 9970
Structural makeupData not provided
Host Sponsor; Other SponsorsRisk Training
Event Site/VenueRadisson Blu Edwardian Bloomsbury Street
Event sold out?No
Multiple locations or dates?No
Statistics available for most recent event held in:NA
Average number in event attendance of:
Range:
Number in attendance of:
Range:
Target BoundariesLocal
Is Registration contact information the same as Organizer Contact above?Yes
Web Page go.evvnt.com/82096-0
Can one register for your CE on your web site?NA
Can registration be made by snail (regular) mail?Yes
Online housing reservations: 
 
Do you have additional written or AV materials for sale?No
If so, are they available for non-attendees as well as those attending?NA
Is full tuition necessary to ensure registration?No
Will there be shuttle service available?NA

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