🚀 go-pugleaf

On 2/24/2018 1:42 PM, Mike Spencer wrote:
> JimP <solo...@gmail.com> writes:
> 
>> On 24 Feb 2018 08:29:08 GMT, Jorgen Grahn <grahn...@snipabacken.se>
>> wrote:
>>
>>> On Sat, 2018-02-24, Charlie Gibbs wrote:
>>>> On 2018-02-23, hanc...@bbs.cpcn.com <hanc...@bbs.cpcn.com> wrote:
>>>>
>>>>> Personally, I think all programmers, whatever their specialty,
>>>>> should have a solid background in algebra.  IMHO, if someone
>>>>> is weak in algebra, they'll be a weak programmer.
>>>
>>> That's the stance my university took, around 1990.  You didn't get to
>>> do computer science until after some hefty maths courses (where most
>>> of the students dropped off).
>>>
>>>> Better still, a solid background in logic.
> 
> My post about the prof who meant to make a course in "computer
> programming" consist of "mumerical methods" wasn't meant to deprecate
> math as background for programming.  Most or all of the people in the
> course were majors in the sciences with attendant requirements for
> calculus, statistics and the like.
> 
> In 1964, programmers weren't building GUIs, designing addictive phone
> apps or data collection scripts for web pages.  (What then passed for)
> big data -- payrolls, say, or policy holder databases -- were just
> getting off the ground, still on cards .  So the prof did have a
> point: If you went into engineering industry as a programmer, you
> might be asked to determine numerically, given blueprints and material
> specs, how much a 90 storey building would sway or aircraft wings
> wobble. Numerical solution of differential equations was the word.
> 
> Notably, Atansoff's ABC was then more recent than the first public web
> server is today.  So the prof could be forgiven for wanting to teach
> the Rung-Kutta method instead of Fortran.  It just wasn't what the
> enrollees in the class were looking for.
> 
> 

At university I took numerical methods one and two... both semesters. 
The examples in the book were FORTRAN... although the professor let us 
use any language for the second semester.  (Except APL...  he wanted our 
code to do the work instead of APL operators!  :-) )

I especially liked Bairstow's Method.  Bairstow's method is an efficient 
algorithm for finding the roots of a real polynomial of arbitrary degree.


https://en.wikipedia.org/wiki/Bairstow%27s_method

https://en.wikipedia.org/wiki/Leonard_Bairstow

-- 
numerist at aquaporin4 dot com