I'm using what is given in the essay, trying to change as little as
possible.  However the polynomial version is more straightforward.

On Fri, Jan 15, 2021 at 11:10 PM Henry Rich <[email protected]> wrote:

> A reasonable expectation.  sslope_jcalculus_ provides an approximation
> to the slope.  A user could write a conjunction to use that if
> deriv_jcalculus_ can't find a closed-form derivative.
>
> But: #.&1 _1 _2&> opens its argument.  Can there ever be a derivative of
> a boxed argument?
>
> Henry Rich
>
> On 1/15/2021 10:48 PM, Devon McCormick wrote:
> > I guess I was expecting a numerical solution if a symbolic one is not
> found.
> >
> > On Fri, Jan 15, 2021 at 10:05 PM Henry Rich <[email protected]>
> wrote:
> >
> >> What do you expect the derivative of #.&1 _1 _2&> to be?
> >>
> >> I see that #.&1 _1 _2 has no derivative, but 1 _1 _2&p. does.
> >>
> >> Deficiencies in math/calculus are not 'issues'.  They are opportunities
> >> for improvement by users.
> >>
> >> Henry Rich
> >>
> >> On 1/15/2021 9:37 PM, Devon McCormick wrote:
> >>> I tried to update chapter 23 of the "50 Shades of J" essay using the
> new
> >>> version of Newton's method from "
> >>> https://code.jsoftware.com/wiki/Essays/Newton%27s_Method"; but this
> >> breaks
> >>> examples in "50 Shades", e.g.
> >>>
> >>>      f1=: #.&1 _1 _2&>   NB. Function to use
> >>>      Newton=: adverb : ']-u % (u deriv_jcalculus_ 1'
> >>>      f1 Newton 1
> >>> |domain error: deriv_jcalculus_
> >>> |       13!:8(3)
> >>> |deriv_jcalculus_[:19]
> >>>
> >>> Is this a known issue?
> >>>
> >>> I've left my "fix" in as the existing code will also break in the
> current
> >>> version of J.
> >>>
> >>
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> >
>
>
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-- 

Devon McCormick, CFA

Quantitative Consultant
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