keywords 
type 
mini sample 
keyword sequence is insignificant 
NUL 
xpr 
NUL=chi() 
required to return a value to SOLVE:
 chi() is an or a that SOLVE tries to minimize. This can be used to determine
 the root(s) of an expression, e.g.:
 the minimum of a function, e.g.:
 the parameters of a function to leastsquare fit some data like
 NUL = ( theory(nr)  data(nr) ) / error(nr)
The function can include for each data point (equation) to account for relative weights or errors, like error(nr) in the example. 
Unknown 
NUM 
u=x 
 scalar argument required for cases with a single root or a single leastsquare fit parameter
 x is the initial guess (default 0) on input, and is the root on output
 the Callback function in NUL=... can be entered directly in this case (e.g. NUL=x^21)

AXIS(MiN=..., MaX=...) and
LINE(..., Points=...) come handy to visualize the equation
 Example: find a root and a function minimum of a (1 root, 1 equation)
 chi2 = SOLVE( Unknown=x, NUL=x^5  x + 1 )
results usually depend on initial x, e.g.:
 initial x > 0.6: x=0.6687397766, chi2=0.2162322132 ()
 initial x ≤ 0.6: x=1.16729231, chi2=9.34876755E9 ()

Unknown 
vec 
u=vec 
 a vector argument is required for cases with m>1 roots. The solution vector must be dimensioned with m elements:
 example of a leastsquare fit to the Gaussian y=h*
EXP(((xx0)/w)^2). Measured data are in the vectors x and y
 parms =
ALIAS(h, x0, w)
! for better legibility form a vector from the 3 parameters
 parms = (8, 5, 2)
! guess some initial parameters for h, x0, w
 chi2 = SOLVE(Unknown=parms,
DATA=m,
DataIdx=nr, NUL=1h*
EXP( ((x(nr)x0)/w)^2)/y(nr) )
 note the normalization of the NUL expression to y(nr) to force equal weights for all data points in the example.
 common practice is also the normalization to the measuring errors.

DataIdx 
NUM 
di=nr 
required for cases with m>1 roots:
 DataIdx is needed to evaluate the correct NUL=... equation in the callback function
 Example:
 XY =
ALIAS(x, y)
! same as
REAL XY(2) with named elements: x == XY(1) and y == XY(2)
 XY = (2, 7)
! define initial values for x and y
 chi2 = SOLVE( U=XY, NUL=null(), DataIdx=nr )
! callback function is null()

! return values are x=7.497601303, y=2.768650594, chi2=3.7395492E7

END
! of "main"

FUNCTION null()
! called by the solver of the SOLVE function. All variables are global
 IF(nr == 1) null = x 
SIN(x)*
COSH(y)
! equation 1
 IF(nr == 2) null = y 
COS(x)*
SINH(y)
! equation 2

END
! of function null()

DATA 
num 
data=d 
 required for a leastsquare fit with less roots (parameters p) than equations (data d)
 for each parameter 1...p the function evaluation NUL=... is called d times
 p is the dimension of vec in Unknown=vec

UnknownIdx 
NUM 
ui=np 
 this root index (or parameter index) can help to debug and organize the callback function
 allows the script to perform indexinvariant but timely part evaluations by testing np, e.g.:
 IF(np == 0)
THEN

! subexpressions valid for >1 parameters could go here
 ENDIF
 np = 0 : return the basechi needed to approximate the derivatives ∂chi/∂root.

Const 
num 
c=2+8 
 optional to keep selected parameters constant while iterationg unstable models
 c=2^(j1) for unknown j to be kept constant, e.g.:
 SOLVE(..., Const=10, ...)
! constant parameters 2 and 4

Limit 
num 
lim=10 
 to limit the next root iteration to root/Limit .... root*Limit
 Limit must be > 1
 default is no Limit (denoted by Limit=0)

MaXIters 
num 
mxi=5 
 maximum number of iterations
 default is MaXIters = 1000

NumDiff 
num 
nc=delta 
 to approximate the differential ∂chi/∂p ≈ (chi(p+delta)  chi(p)) / delta. This can be useful if the callback function involves approximation techniques like numeric
solutions to differential equations
 default NumDiff = 1E5

Speed1 
num 
s1=1e5 
 starting value of the iteration acceleration factor on success
 default Speed1 = 1000

SpeedStop 
num 
ss=1e3 
 to stop iterations after multiple failures
 default SpeedStop = 1e10

ERror 
LBL 
Err=999 
on error jump to an
error label, e.g.:

Iters 
NUM 
i=n 
 returns in n the actual number of iterations performed

SpeedHigh 
NUM 
sh=high 
 returns the maximum successful acceleration factor

SpeedLow 
NUM 
sl=low 
 returns the minimum successful acceleration factor

STDdev 
vec 
std=dp 
 returns a RMS error estimate of Unknown(s)
 dp should have the same dimension as vec in Unknown=vec
 meaningful values only for normally distributed errors
