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simplex rand fifties
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16 changes: 16 additions & 0 deletions _drafts/rand.md
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---
layout: post
title: "simplex, rand, fifties"
---

here's more on links between early digital computing in aerospace (whirlwind, mit instrumentation lab) and economics (rand santa monica). since there were only a handful of computers and coders in the early fifties, it's actually not so surprising, and another puzzle piece fell into place this week. here's a comment from a post back in april.

_covariance matrices, quadratic forms with 'squared units'. the same thing appeared in finance via markowitz portfolio theory at the same time. it would be interesting to know how much contact there was between kalman and markowitz, or whether it was simply an effect of the availability of digital computers and fortran. this also took place only a few years after dantzig and von neumann's work with the simplex algorithm, and it's possible connections can be found between the whole group._

dantzig moved to rand santa monica in 1952 specifically to do the reference implementation of simplex on ibm cpc, 701, 704. it seems probable that most of his work with von neumann was just prior to that, set on the east coast, and focused on less computational concepts such as duality. dantzig was certainly in washington dc working on the seac computer at the national bureau of standards from 1950 to 1952. at that time, laning was creating the fortran prelude 'george' on whirlwind and moving to the mit instrumentation lab to work on missile guidance with battin, where kalman would soon enter the story.

the numerical challenge for simplex was matrix inversion, and the solution came at rand with the revised simplex method and the pfi product form of inverse. the pfi allowed revised simplex to stay in the 'inverse space' and operate on column vectors, the now familiar 'leaving' and 'entering' vectors.

the new puzzle piece is that markowitz joined dantzig at rand, and by 1955 had introduced the alternative to pfi that's still standard today, the efi elimination form of inverse. efi is a mechanisation of the old 'gaussian elimination' taught in every linear algebra course for manual 'by hand' equation solving, but on a deeper level leads to the lu and cholesky factorizations, which are 'square-roots' of a matrix and numerically important for dealing with the 'squared units' of covariance matrices. cholesky was used for the earth gravity field solutions generated by the nasa topex and grace missions at center for space research in austin, and was part of the motivation for bringing cray supercomputers to utexas back in the seventies and eighties.

so dantzig and markowitz were coding simplex at rand mid fifties, at the same time laning, battin, and kalman were coding missile guidance at mit instrumentation lab. how probable is it that they weren't aware of each other, if not actively discussing coding issues?
16 changes: 16 additions & 0 deletions _posts/2024-10-06-simplex-rand-fifties.md
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---
layout: post
title: "simplex, rand, fifties"
---

here's more on links between early digital computing in aerospace (whirlwind, mit instrumentation lab) and economics (rand santa monica). since there were only a handful of computers and coders in the early fifties, it's actually not so surprising, and another puzzle piece fell into place this week. here's a comment from a post back in april.

_covariance matrices, quadratic forms with 'squared units'. the same thing appeared in finance via markowitz portfolio theory at the same time. it would be interesting to know how much contact there was between kalman and markowitz, or whether it was simply an effect of the availability of digital computers and fortran. this also took place only a few years after dantzig and von neumann's work with the simplex algorithm, and it's possible connections can be found between the whole group._

dantzig moved to rand santa monica in 1952 specifically to do the reference implementation of simplex on ibm cpc, 701, 704. it seems probable that most of his work with von neumann was just prior to that, set on the east coast, and focused on less computational concepts such as duality. dantzig was certainly in washington dc working on the seac computer at the national bureau of standards from 1950 to 1952. at that time, laning was creating the fortran prelude 'george' on whirlwind and moving to the mit instrumentation lab to work on missile guidance with battin, where kalman would soon enter the story.

the numerical challenge for simplex was matrix inversion, and the solution came at rand with the revised simplex method and the pfi product form of inverse. the pfi allowed revised simplex to stay in the 'inverse space' and operate on column vectors, the now familiar 'leaving' and 'entering' vectors.

the new puzzle piece is that markowitz joined dantzig at rand, and by 1955 had introduced a particular type of pfi that's still standard today, the efi elimination form of inverse. efi is closely related to the 'gaussian elimination' taught in every linear algebra course for manual 'by hand' equation solving. it leads forward to the lu and cholesky factorizations, which are in a sense 'square-roots' of a matrix and important for dealing with numerical issues in the 'squared units' of covariance matrices. cholesky was used for the earth gravity field solutions generated by the nasa topex and grace missions at center for space research in austin, part of the motivation for bringing cray supercomputers to utexas back in the seventies and eighties.

so dantzig and markowitz were coding simplex at rand mid fifties, at the same time laning, battin, and kalman were coding missile guidance at mit instrumentation lab. how probable is it that they weren't aware of each other, if not actively discussing coding issues?
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21 changes: 13 additions & 8 deletions pdp10/docs/algebra/ipwr.for
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c imaginary powers of 10, starting with 10**(i/1024), and squaring successively ten times.
c this matches with feynman's table 22-3.
integer i
real pi
pi = 3.1415927
i = 0
i = i + 1
10 format (f10.7)
write (*, 10) pi
real x, y, x2, y2
10 format (2f10.5)
y = .00225
x = sqrt(1. - y**2)
do 20 i=1,11
write (6, 10) x, y
x2 = x**2 - y**2
y2 = 2 * x * y
x = x2
y = y2
20 continue
end

68 changes: 68 additions & 0 deletions pdp10/docs/algebra/ipwr.mac
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a=1
b=2
c=3
digit=4
tmp=5
sqr=6
x=10
y=11
x2=12
y2=13
newlin: asciz /
/
start: reset
movei y,^d225
fltr y,y
fmpr y,[0.00001]
move a,y
fmp a,a
movei x,^d1
fltr x,x
fsb x,a
movei sqr,1
sqrloop:jsr nxtsqr
addi sqr,1
caig sqr,13
jrst sqrloop
exit
nxtsqr: 0 ;next iteration on x and y
move a,x
jsr prflt
movei tmp," "
outchr tmp
move a,y
jsr prflt
outstr newlin
move x2,x
fmp x2,x2
move tmp,y
fmp tmp,tmp
fsb x2,tmp
move y2,y
fmp y2,x2
fmp y2,[2.0]
move x,x2
move y,y2
jrstf @nxtsqr
prflt: 0 ;print float less than one in accum a
fix b,a
fltr c,b
fsbr a,c
jumpge b,point
movei tmp,"-"
outchr tmp
point: movei tmp,"."
outchr tmp
movei digit,1
digloop:fmpr a,[10.0]
fix tmp,a
fltr c,tmp
fsbr a,c
addi tmp,"0"
outchr tmp
addi digit,1
caig digit,5
jrst digloop
jrstf @prflt
end start

10 changes: 0 additions & 10 deletions pdp10/docs/algebra/ipwr.py
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feynman's table 22-3. the rounded version gives the same x value in the last iteration as feynman's, -0.66928. note
the initial y = .00225 should be .0022486, just as in the text. the same rounding is used here as there."""
from math import sqrt

print('rounded version')
y = .00225
x = round(sqrt(1. - y**2), 7)
for _ in range(11):
print('%10.5f %10.5f' % (x, y))
x2 = round(x**2 - y**2, 7)
y2 = round(2 * x * y, 7)
x, y = x2, y2
print('\nnon rounded version')
y = .00225
x = sqrt(1. - y**2)
for _ in range(11):
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233 changes: 233 additions & 0 deletions pdp10/docs/algebra/readme.md
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target is using python, fortran10, macro10 in parallel to reproduce [feynman's table 22-3](https://www.feynmanlectures.caltech.edu/I_22.html)

# skip to the punchline

## python

from math import sqrt
y = .00225
x = sqrt(1. - y**2)
for _ in range(11):
print('%10.5f %10.5f' % (x, y))
x2 = x**2 - y**2
y2 = 2 * x * y
x, y = x2, y2

## fortran10

integer i
real x, y, x2, y2
10 format (2f10.5)
y = .00225
x = sqrt(1. - y**2)
do 20 i=1,11
write (6, 10) x, y
x2 = x**2 - y**2
y2 = 2 * x * y
x = x2
y = y2
20 continue
end

## macro10

a=1
b=2
c=3
digit=4
tmp=5
sqr=6
x=10
y=11
x2=12
y2=13
newlin: asciz /
/
start: reset
movei y,^d225
fltr y,y
fmpr y,[0.00001]
move a,y
fmp a,a
movei x,^d1
fltr x,x
fsb x,a
movei sqr,1
sqrloop:jsr nxtsqr
addi sqr,1
caig sqr,13
jrst sqrloop
exit
nxtsqr: 0 ;next iteration on x and y
move a,x
jsr prflt
movei tmp," "
outchr tmp
move a,y
jsr prflt
outstr newlin
move x2,x
fmp x2,x2
move tmp,y
fmp tmp,tmp
fsb x2,tmp
move y2,y
fmp y2,x2
fmp y2,[2.0]
move x,x2
move y,y2
jrstf @nxtsqr
prflt: 0 ;print float less than one in accum a
fix b,a
fltr c,b
fsbr a,c
jumpge b,point
movei tmp,"-"
outchr tmp
point: movei tmp,"."
outchr tmp
movei digit,1
digloop:fmpr a,[10.0]
fix tmp,a
fltr c,tmp
fsbr a,c
addi tmp,"0"
outchr tmp
addi digit,1
caig digit,5
jrst digloop
jrstf @prflt
end start

# stage1

with fortran10, the basic workflow for iterating between raspi and tops10 [is there now](../sec5-minimalist-walkthrough.md). what would be nice is to be able to at the least do 'initial work' on fortran10's old school fortran iv/66 source code in a raspi ide debugger. clearly nothing like this will be possible for macro10, but for fortran10 it's worthwhile.

onboard the raspi, insure that 'sudo apt install gcc' and 'sudo apt install gdb' are go. these cover the gfortran compiler, which does seem able to handle the source code. vscode and its ['modern fortran' extension](https://fortran-lang.org/) also work alright. for vscode run configurations, see [tasks.json](../../.vscode/tasks.json) and [launch.json](../../.vscode/launch.json).

here's the [fortran10](ipwr.for). note in the write (6, 10) the '6' is a 'unit designation' and the '10' is a format statement line number. currently unit designation for terminal is 6 on raspi but 5 on tops10. would like to make this portable, no differences between raspi and tops10.

with macro10, the 'print' of python and 'write' of fortran are the first topic. taking an accumulator containing a floating point number and printing it on the terminal in something like f10.5 format. the pdp10 has sixteen accumulators, and the numerical value contained in one of those is to be printed in 'human readable decimal form' on the terminal.

before that, first step is to do the same but for an accumulator containing a fixed point twos complement binary number. the historical 'decimal output / decout' problem, as discussed in the 'early sixties' section of the levy book, and a kind of historical landmark, along with the contemporary topics of recursion, stack processing, algol, and the beginnings of academic computer science.

a=1
b=2
p=17
pdlen==40
pdlist: block pdlen
opdef call [pushj p,]
opdef ret [popj p,]
crlf: byte (7)15,12
start: reset
move p,[iowd pdlen,pdlist]
movei a,3
call decout
hrroi a,crlf
outstr (a)
exit
decout: jumpge a,decot1
push p,a
movei a,"-"
outchr a
pop p,a
movn a,a
decot1: idivi a,^d10
push p,b
skipe a
call decot1
pop p,a
addi a,"0"
outchr a
ret
end start

# stage2

printing floating point is a modification of the above, bringing in the pdp10 floating point instructions. there's a numerical value to be printed out to the terminal, and that's done one character at a time using the 'outchr' tops10 muuo. this is a request to tops10 to print a character on the terminal, and it's used repeatedly to print each character of the numerical value, including negative sign and decimal point.

as part of understanding all of this, it became apparent that a stack isn't needed. the code above is from the gorin book and it's oriented towards academic computer science. here's a minimalist numerical computing approach. for feynman's table 22-3, the need is to print five digits after the decimal point. that's all this code does. at the start, accumulator 'a' contains a float that is less than one. it's 'after the decimal point'. the machinery repeats five times, cranking five digits out 'in front of the decimal point', one by one, to print them with 'outchr'.

movei tmp,"."
outchr tmp
movei itr,1
loop: fmpr a,[10.0]
fix tmp,a
fltr c,tmp
fsbr a,c
addi tmp,"0"
outchr tmp
addi itr,1
caig itr,5
jrst loop
...

the code below works. it reproduces feynman's table 22-3 and the results of the python and fortran10 code. something interesting happens though, from about the sixth or seventh of the eleven iterations. this is actually an opportunity to learn more about the early days of floating point and its practical use, so will be tackled deliberately going forward. the code below is using standard single precision pdp10 floating point, so 27 bits of precision. the first diagnosis has to be that using double precision would cure the problem. if this turns out to be true, it will be an excellent example of real-world effects of floating point precision, and will mean that feynman's table 22-3 is an excellent detector of poor numerical precision.

a=1
b=2
c=3
digit=4
tmp=5
sqr=6
x=10
y=11
x2=12
y2=13
newlin: asciz /
/
start: reset
movei y,^d225
fltr y,y
fmpr y,[0.00001]
move a,y
fmp a,a
movei x,^d1
fltr x,x
fsb x,a
movei sqr,1
sqrloop:jsr nxtsqr
addi sqr,1
caig sqr,13
jrst sqrloop
exit
nxtsqr: 0 ;next iteration on x and y
move a,x
jsr prflt
movei tmp," "
outchr tmp
move a,y
jsr prflt
outstr newlin
move x2,x
fmp x2,x2
move tmp,y
fmp tmp,tmp
fsb x2,tmp
move y2,y
fmp y2,x2
fmp y2,[2.0]
move x,x2
move y,y2
jrstf @nxtsqr
prflt: 0 ;print float less than one in accum a
fix b,a
fltr c,b
fsbr a,c
jumpge b,point
movei tmp,"-"
outchr tmp
point: movei tmp,"."
outchr tmp
movei digit,1
digloop:fmpr a,[10.0]
fix tmp,a
fltr c,tmp
fsbr a,c
addi tmp,"0"
outchr tmp
addi digit,1
caig digit,5
jrst digloop
jrstf @prflt
end start

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