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I've finally wrapped my head around the advantages and disadvantages of AD/AAD compared to FD and SD, but could someone please explain to me where you'd use AD/AAD in an investment bank/banking generally?

If this question has already been answered, feel free to point me in the right direction.

12Quant34
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    calculation of the "Greeks"

    https://www.boost.org/doc/libs/master/libs/math/doc/html/math_toolkit/autodiff.html#math_toolkit.autodiff.example-black_scholes

     – Charlie S May 28 '21 at 03:08
  • FD is probably 'finite differences', but what is SD? – NNN May 28 '21 at 06:15
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    @Nachiket Symbolic differentiation, I assume. – Federico Poloni May 28 '21 at 07:14
  • Cheers guys, I really appreciate the above! And yes, by SD I meant Symbolic Differentiation (Investment Banking acronymization is clearly rubbing off on me!) – 12Quant34 May 28 '21 at 10:30
  • All neural network learning is essentially an application of automatic differentiation, usually backward, sometimes bidirectional for second derivatives. – Lutz Lehmann May 28 '21 at 16:18
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    Look for M. Giles "Smoking adjoints" (2006) for a (the first?) paper on computing Greeks using AD. – Lutz Lehmann May 28 '21 at 16:24

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