Console manual impl

i
M
P
l

Industrial
Modeling
&
Programming
Language

“Console
Manual"

i
n
d
u
s
t
r
I
A
L
g
o
r
i
t
h
m
s

LLC.

www.industrialgorithms.com

Version
1.0

April
2014

IAL-‐IMPL-‐CM-‐1-‐0.docx

Copyright
and
Property
of
Industrial
Algorithms
LLC.

Introduction

The
IMPL
console
executable
(IMPL.exe)
can
be
called
from
any
DOS
command
prompt
window
where

its
Intel
Fortran
source
code
can
be
found
in
Appendix
A.

The
IMPL
console
is
useful
given
that
it
allows

you
to
model
and
solve
problems
configured
in
an
IML
(Industrial
Modeling
Language)
file.

Problems

coded
using
IPL
(Industrial
Programming
Language)
in
many
computer
programming
languages
can
use

the
IMPL
console
source
code
as
a
prototype.

The
IMPL
console
reads
several
input
files
and
writes
several
output
files
which
are
described
in
this

document.

There
are
several
console
flags
that
can
be
specified
as
command
line
arguments
and
are

described
below.

IMPL
Console
Flags

There
are
twelve
(12)
flags
or
command
line
arguments
that
can
be
specified
and
they
are
as
follows

with
their
respective
possible
values
and
defaults
(shown
in
bolded
italics).

-feed = IMLfile
The
feed
flag
value
must
specify
the
path
and
file
name
(without
the
*.iml
extension
or
file
type)
of
the

IML
file
to
be
modeled
and
solved.

-form = sparsic | symbolic
For
linear
problems,
the
form
flag
values
of
sparsic
and
symbolic
are
equivalent
given
that
the

problem
matrix
has
no
nonlinear
or
non-‐constant
derivatives
or
expressions
and
IMPL
supplies
all
of
the

first-‐order
partial
derivatives
or
coefficients
analytically.

For
nonlinear
problems,
IMPL
represents
the

model
in
a
“sparsic”
form
where
only
the
sparsity-‐pattern
is
supplied
by
IMPL
for
each
constraint
and

numerical
perturbations
are
used
to
compute
the
non-‐constant
or
nonlinear
first-‐order
partial

derivatives
using
machine-‐coded
constraint
residual
calculations
i.e.,
the
constraint
equations
are
hard-‐
coded
into
IMPL’s
native
computer
programming
language.

However,
IMPL
also
supports
a
“symbolic”

form
whereby
the
constraint
residual
calculations
are
computed
using
nonlinear
expressions
supplied
by

IMPL
in
a
byte-‐coded
or
parsed
tokenized
format
sometimes
referred
to
as
postfix
or
reverse
Polish

notation
(RPN).

The
“symbolic”
form
is
useful
given
that
it
can
be
used
to
output
the
nonlinear

constraint
expressions
into
a
human-‐readable
flat-‐file.

-fit = discrete | distributed
The
fit
flag
value
of
discrete assumes
a
uniform
digitization
of
time
i.e.,
discrete-‐time
with
a
single

time-‐period
duration
and
can
be
applied
to
quantity,
logistics
and
quality
types
of
problems
whereas
the

distributed
fit
flag
value
is
not
valid
for
logistics
problems
at
this
time.

IMPL’s
distributed-‐time

formulation
is
similar
to
a
continuous-‐time
model
with
a
common
or
global
time-‐grid
except
that
in
the

former
the
time-‐points
are
known
exogenously.

-filter = quantity | logistics | quality
The
filter
flag
value
of
quantity
assumes
that
all
logic
variables
are
fixed
to
either
0
(zero)
or
1
(one)

and
there
are
no
quality
variables
present
and
solves
a
linear
programming
(LP)
problem.

The
filter
flag

value
of
logistics
solves
a
mixed-‐integer
linear
programming
(MILP)
problem
where
the
logic
variables

are
finite
i.e.,
lie
between
0
or
1.

The
filter
flag
value
of
quality
solves
a
nonlinear
programming
(NLP)

problem
where
the
logic
variables
must
be
fixed
to
either
0
or
1.

A
fourth
filter
flag
value
of

qualogistics
is
possible
but
is
not
available
where
this
would
model
and
solve
a
mixed-‐integer

nonlinear
programming
(MINLP)
problem
(future
implementation).

-focus = simulation | estimation | optimization
The
focus
flag
value
allows
for
simulation
and
estimation
types
of
problems
but
currently
only
the

optimization
value
is
supported.

-factor = 1.0
The
factor
flag
value
is
a
real
positive
number
and
can
be
used
to
scale
quantity
variables
only
i.e.,
rates,

flows
and
holdups.

-factorizer = semisolverless | y12m | ysmp | nspiv | ma28 | pardiso
The
factorizer
flag
value
is
used
to
factorize
or
perform
LU
decomposition
on
a
square
system
or
set
of

linear
and/or
nonlinear
equations
if
it
exists.

If
the
number
of
free
and/or
finite
quantity
and
quality

variables
equals
the
number
of
linear
and
nonlinear
equality
constraints
then
simulation
using
direct

factorization
of
the
sparse
Jacobian
matrix
of
first-‐order
partial
derivatives
can
be
used
to
solve
the

problem
ignoring
all
inequality
constraints
and
variable
bounds.

The
factorizer
flag
pardiso
requires
the

Intel
Math
Kernel
Library
(MKL)
to
execute
a
parallel
implementation
of
LU
decomposition.

-fork = solverless | coinmp | glpk | lpsolve | scip | cplex | gurobi | lindo | xpress
ipopt | conopt | knitro
slpqpe_coinmp | slpqpe_glpk | slpqpe_lpsolve | slpqpe_scip |
slpqpe_cplex | slpqpe_gurobi | slpqpe_lindo | slpqpe_xpress
secqpe_y12m | secqpe_ysmp | secqpe_nspiv | secqpe_ma28 |
secqpe_pardiso
secqpe_sorve | secqpe_y12m_sorve | secqpe_ysmp_sorve |
secqpe_nspiv_sorve | secqpe_ma28_sorve | secqpe_pardiso_sorve
The
fork
flag
value
is
used
to
solve
LP,
QP,
MILP
and
NLP
problems.

The
solvers
slpqpe_
solve
nonlinear

LP
or
QP
problems
using
the
various
LP
and
QP
solvers
suffixed.

The
solvers
secqpe_
solve
nonlinear

equality-‐constrained
QP
problems
using
the
various
LU
decompositions
suffixed
which
are
useful
for

data
reconciliation
and
regression
problems
(i.e.,
least
squares,
error-‐in-‐variables
and
parameter

estimation
types
of
problems).

The
suffixed
solvers
with
_sorve perform
a
post
sensitivity
analysis
to

compute
the
observability,
redundancy
and
variability
estimates
using
the
known
sparse
Jacobian
matrix

from
the
secqpe_
solve.

If
variable
bounds
and
constraint
inequalities
are
required
then
solve
with
an

NLP
first
then
warm-‐start
with
slpqpe_sorve
to
perform
the
post
sensitivity
analysis.

Note
that
the
solvers
scip, cplex, gurobi, lindo, xpress, conopt and
knitro
all
require

commercial
licensing
agreements
from
their
vendors.

-flashback = binaryfile | binaryram | flatfile
The
flashback
flag
value
is
used
to
save
or
retain
integer-‐feasible
solutions
found
during
the
branch-‐and-‐
bound
type
of
enumerative
searches
employed
in
MILP
solvers.

All
integer-‐feasible
solutions
(variable

results
or
responses
only)
are
saved
to
binary
or
unformatted
files
with
extension
*.bdt
where
a
“_n”
is

suffixed
to
the
IML
file
problem
name
with
“n”
denoting
the
solution
number
found.

The
flashback
flag

value
of
binaryram
means
that
the
solution
is
also
saved
to
RAM
memory
and
a
value
of
flatfile
will

save
the
solution
in
a
formatted
*.exl
file.

-fanfare = 1073741823 (2^30 - 1)

The
fanfare
flag
value
is
an
integer
bit
mask
with
0
to
30
bits.

Depending
on
the
value
of
fanfare,
the

various
IMPL
output
files
will
be
written
to
the
same
directory
as
the
IML
file.

For
more
details
please

refer
to
Appendix
A.

-fuse = coldstart | warmstart
The
fuse
flag
value
is
only
valid
for
nonlinear
problems
where
a
warmstart
will
use
the
solution
variable

result
values
from
a
previous
solve
(if
they
exist)
as
initial-‐values
or
starting-‐points
for
the
next
solve.

-frequency = 0
The
frequency
flag
value
sets
the
number
of
solving
cycles
within
a
solving
execution
or
run.

It
is

essentially
a
do-‐loop
starting
from
1
to
frequency
which
will
randomize
the
initial-‐values,
starting-‐points

or
default-‐results
starting
from
the
random
seed
setting
and
simply
incrementing
it
by
the
loop
iterate

or
counter
i.e.,
initial_value = (lower_bound+upper_bound)/2 + (upper_bound-
lower_bound)*random_number(random_seed).

IMPL
Input
Files

IMPL.lic
License
file
for
IMPL
that
must
be
located
in
the
same
directory
as
the
IMPL
executable.

IMPL.set | *.set
Settings
file
for
IMPL
that
may
be
located
in
the
same
directory
as
the
IMPL
executable
or
another
*.set

file
located
in
the
same
directory
as
the
IML
file
with
the
same
name.

IMPL.mem | *.mem
Memory
file
for
IMPL
that
may
be
located
in
the
same
directory
as
the
IMPL
executable
or
another

*.mem
file
located
in
the
same
directory
as
the
IML
file
with
the
same
name.

The
memory
file
is
used
to

allocate
the
various
resource-‐entities
of
IMPL.

IMPL.solver | *.solver
Individual
solver
settings
files
for
the
various
IMPL
solvers
and
they
may
be
located
in
the
same
directory

as
the
IMPL
executable
or
another
*.solver
file
located
in
the
same
directory
as
the
IML
file
with
the

same
name.

*.iml
Industrial
(input)
modeling
language
files
containing
the
IML
frames
configuring
a
problem.

*.oml
Output
modeling
language
files
with
the
same
name
and
location
as
the
IML
file
where
selected
variable

results
and
constraint
residuals
can
be
written
to
user-‐specified
files.

*.ups
UOPSS
files
(or
universal
production/process
superstructure)
written
by
the
IALConstructer.py
Python

2.3.5
code
found
in
the
Dia
open-‐source
application
which
can
be
included
into
any
IMF
file.

This
*.ups

file
can
contain
the
complete
or
partial
construction
data
of
the
network,
graph,
diagram
or
flowsheet
of

the
problem.

*.ilp | *.inl
Foreign
files
with
scalar-‐based
variable
and
constraint
names
can
be
used
to
extend
the
IMPL
modeling

with
user-‐created
and/or
shared
variables
and
user-‐created
constraints.

The
format
of
these
files
is

similar
to
the
CPLEX
LP
files
where
for
nonlinear
model
extensions,
the
*.inl
file
allows
nonlinear

expressions
to
be
specified.

If
a
quantity
or
logistics
(LP,
QP
or
MILP)
problem
is
being
solved
then
only

the
*.ilp
is
recognized
and
if
a
quality
(NLP)
problem
is
being
solved
then
only
*.inl
files
are
respected.

Both
the
*.ilp
and
*.inl
files
include
the
following
sections:
“Objective”,
“Constraints”
and
“Bounds”.

There
is
also
a
“Binaries”
section
for
*.ilp
files
only
and
both
files
must
have
the
last
statement
as
“End”

where
in-‐line
comments
are
indicated
by
the
“”
backslash
character.

IMPL
Output
Files

*.io
Input
and
output
file
containing
the
inputs
and
outputs
for
each
IML
file
problem
UOPSS
superstructure.

*.iod
Input
and
output
data
file
containing
the
input
and
output
data
for
each
IML
file
problem
UOPSS

superstructure.

*.oi
Output
and
input
file
containing
the
outputs
to
inputs
for
each
IML
file
problem
UOPSS
superstructure.

*.exl
Export
files
with
the
same
name
and
location
as
the
IML
file
where
selected
variable
result
values
are

written
in
various
EXL
frames
similar
to
the
IML
frames.

*.bdt
Binary
(unformatted)
files
storing
the
independent
sets,
catalogs,
lists,
parameters
and
formulas.

These

unformatted
files
are
useful
when
re-‐running
IMPL
without
having
to
re-‐read
the
*.iml
file.

*_n.bdt
Binary
(unformatted)
files
suffixed
by
an
integer-‐feasible
solution
number
“_n”
with
the
same
name
and

location
as
the
IML
file
where
all
variable
results
for
a
single
integer-‐feasible
solution
found
in
the
MILP

solvers
are
written
in
unformatted
form.

*.ldt
Log
files
with
the
same
name
and
location
as
the
IML
file
where
IMPL
progress
messages
are
displayed
if

USELOGFILE = 1
is
set
else
all
messages
will
be
sent
to
the
console
or
command
prompt
window.

*.rdt
Report
files
with
the
same
name
and
location
as
the
IML
file
where
IMPL
resource-‐entity
details
are

displayed.

*.sdt
Summary
files
with
the
same
name
and
location
as
the
IML
file
where
IMPL
modeling
and
presolving

details
are
displayed
such
as
the
number
of
variables,
constraints
and
derivatives
included
or
excluded

from
the
problem.

*.tdt
Statics
files
with
the
same
name
and
location
as
the
IML
file
where
if
any
IMPL
presolved
constraints
are

found
to
be
infeasible
(i.e.,
violated
greater
than
a
tolerance)
at
a
solution,
are
displayed.

“Static”

constraints
are
IMPL’s
term
for
constraints
that
are
presolved
or
removed
from
the
problem
by
IMPL

before
the
solvers
(with
their
own
presolving)
are
called
where
“dynamic”
constraints
in
this
context

mean
constraints
that
are
variable
in
the
solver.

The
IMPL
presolver
may
or
may
not
“prempt”
the

presolving
if
an
infeasible
or
inconsistent
constraint
is
found.

*.jdt
Sensitivity
or
derivatives
files
with
the
same
name
and
location
as
the
IML
file
where
the
first-‐order

partial
derivative
sparse
Jacobian
matrix
elements
are
displayed.

*.ndt
Symbolic
or
mnemonic
files
with
the
same
name
and
location
as
the
IML
file
where
a
human-‐readable

form
of
both
the
linear
and
nonlinear
constraints
are
displayed
for
every
variable
and
constraint
known

to
the
problem.

*.imp, *.imv, *.imc
Three
files
containing
all
of
the
parameters
(including
all
of
the
sets,
catalogs,
lists
and
formulas),

variables
and
constraints
created
by
IMPL
to
model
the
problem.

These
files
can
be
used
to
reference

the
names
found
in
the
*.dta
files
below.

*.dtr
Series-‐set
(or
range-‐set)
files
with
the
same
name
and
location
as
the
IML
file
where
all
SERIES-‐SET

resource-‐entity
roster-‐enumerations
are
output.

*.dts
Simple-‐set
files
with
the
same
name
and
location
as
the
IML
file
where
all
SIMPLE-‐SET
resource-‐entity

are
output.

*.dty
Symbol-‐set

files
with
the
same
name
and
location
as
the
IML
file
where
all
SYMBOL-‐SET
resource-‐entity

are
output.

*.dtg
Catalog
files
with
the
same
name
and
location
as
the
IML
file
where
all
CATALOG
resource-‐entity
roster-‐
enumerations
are
output.

*.dtl
List
files
with
the
same
name
and
location
as
the
IML
file
where
all
LIST
resource-‐entity
roster-‐
enumerations
are
output.

*.dtp
Parameter

files
with
the
same
name
and
location
as
the
IML
file
where
all
PARAMETER
resource-‐entity

are
output.

*.dtv
Variable

files
with
the
same
name
and
location
as
the
IML
file
where
all
VARIABLE
resource-‐entity

are
output.

*.dtw
“Vetistic”
(vetting)
or
statistic
files
with
the
same
name
and
location
as
the
IML
file
where
all
VARIABLE

and
CONSTRAINT
resource-‐entity
are
output
when
the
solvers
suffixed
with
_sorve

are
used.

This
file
outputs
the
observability
and
redundancy
metrics,
variances,
“maximum
power”

gross-‐error
detection
statistics
and
confidence-‐intervals.

*.dtc
Constraint

files
with
the
same
name
and
location
as
the
IML
file
where
all
CONSTRAINT
resource-‐entity

are
output.

*.dtf
Formula

files
with
the
same
name
and
location
as
the
IML
file
where
all
FORMULA
resource-‐entity

are
output.

Appendix
A
–
IMPL
Console
Source
Code
in
Intel
Fortran
(IMPL.exe)

Provided
below
is
the
Intel
Fortran
source
code
which
calls
the
various
routines
found
in
the
IMPL

system
architecture
SIIMPLE
(Server,
Interacter,
Interfacer,
Modeler,
Presolver
Libraries).

We
have

included
this
source
code
to
show
how
to
call
IMPL
with
SIIMPL
from
other
computer
programming

languages
such
as
C,
C++,
C#,
Java,
Python,
Excel/VBA,
etc.

program IMPL
use IFWIN
use IMPLserver
cccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccc
c INDUSTRIAL ALGORITHMS LLC. CONFIDENTIAL & PROPRIETARY cccccccccccccccccccccccc
implicit none
c Project "feed" data name which also includes the path.
character(BASESTRINGLEN*LINESTRINGLENMULT) :: feed
c Problem "fact" data name which also includes the path.
character(BASESTRINGLEN*LINESTRINGLENMULT) :: fact
c Data "face" or stream i.e., either "import/out" or "export/in".
integer(4) :: face
c Model "factor" or scaling for dominant or primary phenomenological dimension.
real(8) :: factor
c Model "sipher" (cipher, secret-code) for encryption and deencryption.
integer(8) :: fob
c Model "form "or structure i.e., either "sparsic" or "symbolic".
integer(4) :: form
c Model "fit" or slot usually the temporal dimension.
integer(4) :: fit
c Model "filter" or select.
integer(4) :: filter
c Model "focus" or syllabus.
integer(4) :: focus
c Model "filler" or supplemental pointing to the supplierlib() routine.
integer(4) :: filler
c Model "foreign" ILP or INL file.
character(BASESTRINGLEN*LINESTRINGLENMULT) :: foreign
c Semi-solve "factorizer" or semi-solver.
integer(4) :: factorizer
c Solve "fork" or solver.
integer(4) :: fork
c Solve fresh or "stale".
integer(4) :: fresh
c Savings format for intermediate integer-feasible solutions when they are found during the search.
integer(4) :: flashback
c Feedback or callback "summons" routine.
integer(4) :: feedback
c Model and solve fanfare or "show".
integer(4) :: fanfare
c Solve from a previously solved solution (and not necessarily converged) i.e., "fuse" this new solution
c with a preceding solution ("warm-start" v. "cold-start").
integer(4) :: fuse
c
Rerun/restart/repeat
the
modeling
and
solving
in
a
loop
a
specified
number
of
times
by
randomizing
the
initial-‐

c
values
or
starting-‐points
using
the
random-‐seed.

c

c
The
frequency
("stride")
is
the
number
of
cycles
plus
one
(+1)
per
execution
or
run
of
this
console
program
where

c
zero
(0)
means
only
the
initial,
starting
or
default
run
is
performed.

integer(4)
::
frequency

integer(4)
::
rseed

c Data frames separated by the ASCII NULL character i.e., CHAR(0) in fortran or "/n" in C.
character(BASESTRINGLEN*PAGESTRINGLENMULT) :: frames
integer(4) :: flag
c Command line argument string value.
character(BASESTRINGLEN*LINESTRINGLENMULT) :: sargument, sargument1, sargument2
interface
function IMPLinterfaceri(fact,form,fit,filter,focus,face,factor,fob,frames)
#if stdcalling == 1
cDEC$ ATTRIBUTES DLLIMPORT, STDCALL, REFERENCE, DECORATE, ALIAS : "IMPLinterfaceri" :: IMPLINTERFACERI
#else
cDEC$ ATTRIBUTES DLLIMPORT, ALIAS : "IMPLinterfaceri" :: IMPLINTERFACERI
#endif
integer(4) :: IMPLinterfaceri
character(*), intent(in) :: fact
cDEC$ ATTRIBUTES REFERENCE :: fact
integer(4), intent(in) :: form
cDEC$ ATTRIBUTES VALUE :: form
integer(4), intent(in) :: fit
cDEC$ ATTRIBUTES VALUE :: fit
integer(4), intent(in) :: filter
cDEC$ ATTRIBUTES VALUE :: filter
integer(4), intent(in) :: focus
cDEC$ ATTRIBUTES VALUE :: focus
integer(4), intent(in) :: face
cDEC$ ATTRIBUTES VALUE :: face
real(8), intent(in) :: factor
cDEC$ ATTRIBUTES VALUE :: factor
integer(8), intent(in) :: fob
cDEC$ ATTRIBUTES VALUE :: fob
character(*), intent(in) :: frames
cDEC$ ATTRIBUTES REFERENCE :: frames
end function IMPLinterfaceri
function IMPLinterfacere(fact,form,fit,filter,focus,face,factor,fob,frames)
#if stdcalling == 1
cDEC$ ATTRIBUTES DLLIMPORT, STDCALL, REFERENCE, DECORATE, ALIAS : "IMPLinterfacere" :: IMPLINTERFACERE
#else
cDEC$ ATTRIBUTES DLLIMPORT, ALIAS : "IMPLinterfacere" :: IMPLINTERFACERE
#endif
integer(4) :: IMPLinterfacere
integer(4), intent(in) :: face
cDEC$ ATTRIBUTES VALUE :: face
real(8), intent(in) :: factor
cDEC$ ATTRIBUTES VALUE :: factor
integer(8), intent(in) :: fob
cDEC$ ATTRIBUTES VALUE :: fob
character(*), intent(in) :: frames
cDEC$ ATTRIBUTES REFERENCE :: frames
end function IMPLinterfacere
function IMPLmodelerv(fact,form,fit,filter,focus,filler,foreign,force)
#if stdcalling == 1
cDEC$ ATTRIBUTES DLLIMPORT, STDCALL, REFERENCE, DECORATE, ALIAS : "IMPLmodelerv" :: IMPLMODELERV
#else
cDEC$ ATTRIBUTES DLLIMPORT, ALIAS : "IMPLmodelerv" :: IMPLMODELERV
#endif
integer(4) :: IMPLmodelerv
integer(4), intent(in) :: filler
cDEC$ ATTRIBUTES REFERENCE :: filler
character(*), optional, intent(in) :: foreign
cDEC$ ATTRIBUTES REFERENCE :: foreign
integer(4), optional, intent(in) :: force
cDEC$ ATTRIBUTES REFERENCE :: force
end function IMPLmodelerv
function IMPLmodelerc(fact,form,fit,filter,focus,filler,foreign,force)
#if stdcalling == 1
cDEC$ ATTRIBUTES DLLIMPORT, STDCALL, REFERENCE, DECORATE, ALIAS : "IMPLmodelerc" :: IMPLMODELERC
#else

cDEC$ ATTRIBUTES DLLIMPORT, ALIAS : "IMPLmodelerc" :: IMPLMODELERC
#endif
integer(4) :: IMPLmodelerc
integer(4), intent(in) :: filler
cDEC$ ATTRIBUTES REFERENCE :: filler
character(*), optional, intent(in) :: foreign
cDEC$ ATTRIBUTES REFERENCE :: foreign
integer(4), optional, intent(in) :: force
cDEC$ ATTRIBUTES REFERENCE :: force
end function IMPLmodelerc
function IMPLpresolver(fact,form,fit,filter,focus,factorizer,fork,fresh,flashback,feedback)
#if stdcalling == 1
cDEC$ ATTRIBUTES DLLIMPORT, STDCALL, REFERENCE, DECORATE, ALIAS : "IMPLpresolver" :: IMPLPRESOLVER
#else
cDEC$ ATTRIBUTES DLLIMPORT, ALIAS : "IMPLpresolver" :: IMPLPRESOLVER
#endif
integer(4) :: IMPLpresolver
integer(4), intent(in) :: factorizer
cDEC$ ATTRIBUTES VALUE :: factorizer
integer(4), intent(in) :: fork
cDEC$ ATTRIBUTES VALUE :: fork
integer(4), intent(in) :: fresh
cDEC$ ATTRIBUTES VALUE :: fresh
integer(4), intent(in) :: flashback
cDEC$ ATTRIBUTES VALUE :: flashback
integer(4), intent(in) :: feedback
cDEC$ ATTRIBUTES REFERENCE :: feedback
end function IMPLpresolver
end interface
c Integer pointer to modeling subroutine location.
integer(4) :: ptr_IMPLmodelerc
c Temporary allocatable storage when required.
integer(4), allocatable :: ival(:)
real(8), allocatable :: rval(:)
character(BASESTRINGLEN), allocatable :: sval(:)
c Miscellaneous.
integer(4) :: i, rtnstat, lun
character(BASESTRINGLEN*LINESTRINGLENMULT) :: logmessage
character(BASESTRINGLEN) :: setting
character(BASESTRINGLEN) :: startdate, finishdate, sizelimits, hostname, username
c Timings for performance monitoring or profiling.
c
c * Note that to compute running times then use the following code.
c
c ftime = DCLOCK()
c dttime = ftime-stime
c ttime = ttime+dttime
c stime = ftime
c
c * Note that CPU_TIME() returns the sum of time over all threads which does return the elapsed time.
c Instead DCLOCK() returns the elapsed time and requires module IFPORT.
real(8) :: stime, ftime, dttime, ttime, ttime2
c Start the clock.
ttime = 0
stime = DCLOCK()
c Specify a version number for which will be maintained as a standalone entity.
c
c Year.Month.DayIncrement i.e., 2007-12-05 5th increment per day = 7.12.055 (major.minor.update release).
c
c This versioning scheme is simple and accurate and only useful for tracking changes continuously. It is

c not a snapshot or build release number with "major.minor.patch" release structure.
c * Note that "-1" means to write to standard output which is usually the screen.
rtnstat = IMPLwritebanner(-1)
write(*,"(1X,A)")"<<< i M P l - E x e c u t a b l e (console)"
write(*,"(1X,A)")"<<<"
write(*,"(1X,A)")"<<< Release # 1.0"
write(*,"(1X,A)")"<<< Version # 12.5.141"
write(*,"(1X,A)")"<<<"
write(*,"(1X,4A)")"<<< Last Compiled: ",__DATE__," ",__TIME__
write(*,"(1X,A)")"<<<"
c Parse the command line arguments for the required flags.
feed = "c:IMPLIMLfile"
fact = feed
form = SPARSIC$
! form = SYMBOLIC$
fit = DISCRETE$
! fit = DISTRIBUTE$
filter = QUANTITY$
! filter = LOGISTICS$
! filter = QUALITY$
c Simulation, Estimation and Optimization "Interoperability".
! focus = SIMULATION$
! focus = ESTIMATION$
focus = OPTIMIZATION$
factor = 1d+0
factorizer = SEMISOLVERLESS$
! factorizer = Y12M$
! factorizer = YSMP$
! factorizer = NSPIV$
! factorizer = MA28$
! factorizer = PARDISO$
fork = SOLVERLESS$
! fork = COINMP$
! fork = GLPK$
! fork = LPSOLVE$
! fork = SCIP$
! fork = CPLEX$
! fork = GUROBI$
! fork = LINDO$
! fork = OPTONOMY$
! fork = XPRESS$
!SQP's
! fork = IPOPT$
! fork = CONOPT$
! fork = KNITRO$
! fork = NOVA$
! fork = WORHP$
!SLP's
! fork = SLPQPE_COINMP$
! fork = SLPQPE_GLPK$
! fork = SLPQPE_LPSOLVE$
! fork = SLPQPE_SCIP$
! fork = SLPQPE_CPLEX$
! fork = SLPQPE_GUROBI$
! fork = SLPQPE_OPTONOMY$
! fork = SLPQPE_XPRESS$
! fork = SLPQPE_IPOPT$
! fork = SECQPE_Y12M$
! fork = SECQPE_YSMP$
! fork = SECQPE_NSPIV$
! fork = SECQPE_MA28$
! fork = SECQPE_PARDISO$
! fork = SECQPE_SORVE$
! fork = SECQPE_Y12M_SORVE$
! fork = SECQPE_YSMP_SORVE$
! fork = SECQPE_NSPIV_SORVE$
! fork = SECQPE_MA28_SORVE$
! fork = SECQPE_PARDISO_SORVE$
c Suitability (svve), Steadyability (ssde) and Scrumability (sfcme).
! fork = SVVE$
! fork = SSDE$
! fork = SFCME$

fanfare = SHOWALL$
fuse = COLDSTART$
! fuse = WARMSTART$
frequency = 0
fob = 0
frames = CHAR(0)
c Get the library and function addresses for the filler, supplemental, external modeler, etc. which will provide the
c necessary modeling for "black-blank" unit-operations.
c
c * Note that if the filler or supplemental routine is not found, then the return status is either negative or zero (0) and
c hence filler = 0.
filler = loadlibrary("C:IndustrialAlgorithmsPlatformIMPLIMPLsupplierDebugIMPLsupplier.dll"//CHAR(0))
if (filler > 0) then
filler = getprocaddress(filler,"IMPLsupplierlib"//CHAR(0))
else
filler = 0
end if
fresh = FIRSTSESSION$
c Save, cache, store, persist, etc. integer-feasible solutions to an *.exl file as well as the default *.bdt binary file.
flashback = FLATFILE$
c Point to the "recall()" routine provided by the "server".
feedback = LOC(IMPLrecall)
c The command line arguments (flags) can be specified as follows:
c
c * Note that if the second argument (after the "=" sign) is not recognizable as a valid value then an exception or
c error occurs.
c
c impl.exe -feed=""
c -form=sparsic
c -fit=discrete
c -filter=logistics
c -focus=optimization
c -factor=1
c -factorizer=y12m
c -fork=lpsolve
c -flashback=flatfile
c -fanfare=1073741823
c -fuse=cold
c -frequency=0
do i = 1,COMMAND_ARGUMENT_COUNT()
call GET_COMMAND_ARGUMENT(i,sargument)
sargument1 = sargument(1:INDEX(sargument,"="))
sargument2 = sargument(INDEX(sargument,"=")+1:BASESTRINGLEN*LINESTRINGLENMULT)
select case (sargument1)
case ("-feed=")
if (TRIM(sargument2) /= "") then
feed = TRIM(sargument2)
fact = feed
else
write(*,*)"<<< %ERROR% - unknown -feed= argument present."
stop
end if
write(*,*)"<<< feed = ",TRIM(sargument2)
write(*,*)"<<< fact = ",TRIM(sargument2)
case ("-form=")
if (TRIM(sargument2) == "sparsic") then
form = SPARSIC$
elseif (TRIM(sargument2) == "symbolic") then
form = SYMBOLIC$
else
write(*,*)"<<< %ERROR% - unknown -form= argument present."
stop
end if
write(*,*)"<<< form = ",TRIM(sargument2)," = ",form
case ("-fit=")
if (TRIM(sargument2) == "discrete") then
fit = DISCRETE$
elseif (TRIM(sargument2) == "distribute") then
fit = DISTRIBUTE$
else
write(*,*)"<<< %ERROR% - unknown -fit= argument present."

stop
end if
write(*,*)"<<< fit = ",TRIM(sargument2)," = ",fit
case ("-filter=")
if (TRIM(sargument2) == "quantity") then
filter = QUANTITY$
elseif (TRIM(sargument2) == "logistics") then
filter = LOGISTICS$
elseif (TRIM(sargument2) == "quality") then
filter = QUALITY$
else
write(*,*)"<<< %ERROR% - unknown -filter= argument present."
stop
end if
write(*,*)"<<< filter = ",TRIM(sargument2)," = ",filter
case ("-focus=")
if (TRIM(sargument2) == "simulation") then
focus = SIMULATION$
elseif (TRIM(sargument2) == "estimation") then
focus = ESTIMATION$
elseif (TRIM(sargument2) == "optimization") then
focus = OPTIMIZATION$
else
write(*,*)"<<< %ERROR% - unknown -focus= argument present."
stop
end if
write(*,*)"<<< focus = ",TRIM(sargument2)," = ",focus
case ("-factor=")
read(sargument2,*)factor
factor = ABS(MIN(INFIN,factor))
else
write(*,*)"<<< %ERROR% - no -factor= argument present."
stop
end if
write(*,*)"<<< factor = ",factor
case ("-factorizer=")
if (TRIM(sargument2) == "") then
factorizer = SEMISOLVERLESS$
elseif (TRIM(sargument2) == "y12m") then
factorizer = Y12M$
elseif (TRIM(sargument2) == "ysmp") then
factorizer = YSMP$
elseif (TRIM(sargument2) == "nspiv") then
factorizer = NSPIV$
elseif (TRIM(sargument2) == "ma28") then
factorizer = MA28$
elseif (TRIM(sargument2) == "pardiso") then
factorizer = PARDISO$
else
write(*,*)"<<< %ERROR% - unknown -factorizer= argument present."
stop
end if
write(*,*)"<<< factorizer = ",TRIM(sargument2)," = ",factorizer
case ("-fork=")
fork = SOLVERLESS$
elseif (TRIM(sargument2) == "coinmp") then
fork = COINMP$
elseif (TRIM(sargument2) == "glpk") then
fork = GLPK$
elseif (TRIM(sargument2) == "lpsolve") then
fork = LPSOLVE$
elseif (TRIM(sargument2) == "scip") then
fork = SCIP$
elseif (TRIM(sargument2) == "cplex") then
fork = CPLEX$
elseif (TRIM(sargument2) == "gurobi") then
fork = GUROBI$
elseif (TRIM(sargument2) == "xpress") then
fork = XPRESS$
elseif (TRIM(sargument2) == "conopt") then
fork = CONOPT$
elseif (TRIM(sargument2) == "ipopt") then
fork = IPOPT$
elseif (TRIM(sargument2) == "knitro") then
fork = KNITRO$
elseif (TRIM(sargument2) == "nova") then
fork = NOVA$
elseif (TRIM(sargument2) == "worhp") then

fork = WORHP$
elseif (TRIM(sargument2) == "slpqpe_coinmp") then
fork = SLPQPE_COINMP$
elseif (TRIM(sargument2) == "slpqpe_glpk") then
fork = SLPQPE_GLPK$
elseif (TRIM(sargument2) == "slpqpe_lpsolve") then
fork = SLPQPE_LPSOLVE$
elseif (TRIM(sargument2) == "slpqpe_scip") then
fork = SLPQPE_SCIP$
elseif (TRIM(sargument2) == "slpqpe_cplex") then
fork = SLPQPE_CPLEX$
elseif (TRIM(sargument2) == "slpqpe_gurobi") then
fork = SLPQPE_GUROBI$
elseif (TRIM(sargument2) == "slpqpe_optonomy") then
fork = SLPQPE_OPTONOMY$
elseif (TRIM(sargument2) == "slpqpe_xpress") then
fork = SLPQPE_XPRESS$
elseif (TRIM(sargument2) == "secqpe_y12m") then
fork = SECQPE_Y12M$
elseif (TRIM(sargument2) == "secqpe_ysmp") then
fork = SECQPE_YSMP$
elseif (TRIM(sargument2) == "secqpe_nspiv") then
fork = SECQPE_NSPIV$
elseif (TRIM(sargument2) == "secqpe_ma28") then
fork = SECQPE_MA28$
elseif (TRIM(sargument2) == "secqpe_pardiso") then
fork = SECQPE_PARDISO$
elseif (TRIM(sargument2) == "secqpe_sorve") then
fork = SECQPE_SORVE$
elseif (TRIM(sargument2) == "secqpe_y12m_sorve") then
fork = SECQPE_Y12M_SORVE$
elseif (TRIM(sargument2) == "secqpe_ysmp_sorve") then
fork = SECQPE_YSMP_SORVE$
elseif (TRIM(sargument2) == "secqpe_nspiv_sorve") then
fork = SECQPE_NSPIV_SORVE$
elseif (TRIM(sargument2) == "secqpe_ma28_sorve") then
fork = SECQPE_MA28_SORVE$
elseif (TRIM(sargument2) == "secqpe_pardiso_sorve") then
fork = SECQPE_PARDISO_SORVE$
else
write(*,*)"<<< %ERROR% - unknown -fork= argument present."
stop
end if
write(*,*)"<<< fork = ",TRIM(sargument2)," = ",fork
case ("-flashback=")
flashback = BINARYFILE$
elseif (TRIM(sargument2) == "binaryfile") then
flashback = BINARYFILE$
elseif (TRIM(sargument2) == "binaryram") then
flashback = BINARYRAM$
elseif (TRIM(sargument2) == "flatfile") then
flashback = FLATFILE$
else
write(*,*)"<<< %ERROR% - unknown -flashback= argument present."
stop
end if
write(*,*)"<<< flashback = ",TRIM(sargument2)," = ",flashback
case ("-fanfare=")
read(sargument2,*)fanfare
fanfare = ABS(fanfare)
show$ = fanfare
end if
write(*,*)"<<< fanfare = ",TRIM(sargument2)," = ",fanfare
case ("-fuse=")
if (TRIM(sargument2) == "cold") then
fuse = COLDSTART$
elseif (TRIM(sargument2) == "warm") then
fuse = WARMSTART$
else
write(*,*)"<<< %ERROR% - unknown -fuse= argument present."
stop
end if
write(*,*)"<<< fuse = ",TRIM(sargument2)," = ",fuse
case ("-frequency=")
read(sargument2,*)frequency
frequency = ABS(frequency)
else
write(*,*)"<<< %ERROR% - no -frequency= argument present."
stop
end if

write(*,*)"<<< frequency = ",frequency
end select
end do
show$ = fanfare
start$ = fuse
stride$ = frequency
c Specify the foreign ILP or INL file and if it exists, it will be used to augment or extend the matrix.
foreign = TRIM(fact)//".ilp"
if ((filter == QUALITY$) .or. (filter == QUALOGISTICS$)) then
foreign = TRIM(fact)//".inl"
end if
supplementary$ = foreign
c If "symbolic-form" specified but the solving-system is of the "sparsic-form" only then solve the
c "sparsic-form".
c
c * Note that at the moment only IPOPT and SLPQPE can be of both the "sparsic" and "symbolic" forms.
if ((fork == CONOPT$) .or. (fork == KNITRO$) .or.
& (fork == NOVA$) .or. (fork == WORHP$)) then
form = SPARSIC$
end if
c Only allow "symbolic-form" with XPRESS-SLP and LINDO-SLP given that these are not possible to be called
c using "sparsic-form".
if ((fork == XPRESS$) .or. (fork == LINDO$)) then
form = SYMBOLIC$
end if
c Only allow discrete-time for logistics.
if (filter == LOGISTICS$) then
fit = DISCRETE$
end if
c Only allow our nonlinear SLPQPE_ solver as the fork if filter equal "quality" and a LP solver has been specified.
if (filter == QUALITY$) then
if (fork == COINMP$) then
fork = SLPQPE_COINMP$
elseif (fork == GLPK$) then
fork = SLPQPE_GLPK$
elseif (fork == LPSOLVE$) then
fork = SLPQPE_LPSOLVE$
elseif (fork == SCIP$) then
fork = SLPQPE_SCIP$
elseif (fork == CPLEX$) then
fork = SLPQPE_CPLEX$
elseif (fork == GUROBI$) then
fork = SLPQPE_GUROBI$
end if
end if
write(*,*)"<<<"
write(*,*)"<<<",TRIM(fact)
write(*,*)"<<<"
c Initialize or establish the environment using the settings and license files.
rtnstat = IMPLroot(fact)
if (rtnstat == INNON) then
write(*,*)"<<< %ERROR% - Invalid or missing license file."
stop
elseif (rtnstat > 0) then
write(*,*)"<<< %ERROR% - Missing settings file."
stop
elseif (rtnstat < 0) then
write(*,*)"<<< %WARNING% - Number of missing settings = ",-rtnstat
end if
logmessage = "<<<"
rtnstat = IMPLwritelog(logmessage)
logmessage = "<<< Root successful."
c Initialize or allocate the global memory using the memory file.
rtnstat = IMPLreserve(fact,ALL)
write(*,*)"<<< %ERROR% - Invalid or missing license file."
stop
write(*,*)"<<< %ERROR% - Missing memory file."
stop

write(*,*)"<<< %WARNING% - Missing memory setting."
end if
logmessage = "<<< Reserve successful."
ftime = DCLOCK()
dttime = ftime-stime
ttime = ttime+dttime
stime = ftime
write(*,*)"<<<"
write(*,*)"<<< Initialization time = ",dttime
write(*,*)"<<<"
c "Interface" the industrial/manufacturing optimization problem data.
c
c Interfacing the problem means reading in, inputing or importing the problem from the industrial/manufacturing
c modeling language file (*.iml).
face = IMPORT$
frames = CHAR(0)
rtnstat = IMPLinterfaceri(fact,form,fit,filter,focus,face,factor,fob,frames)
write(*,*)"<<< %ERROR% - Invalid arguments."
stop
write(*,*)"<<< %ERROR% - Issue parsing import-file on line = ",rtnstat
stop
write(*,*)"<<< %WARNING% - Issue parsing import-file on line = ",ABS(rtnstat)
end if
rtnstat = IMPLreassure()
if (rtnstat /= 0) then
write(*,*)"<<< %ERROR% - Memory violation for resource-entity type = ",TRIM(IMPLreason(rtnstat))
stop
end if
logmessage = "<<< Reassure successful."
ftime = DCLOCK()
stime = ftime
write(*,*)"<<<"
write(*,*)"<<< Import time = ",dttime
write(*,*)"<<<"
c "Serialize" or persist the problem-data i.e., sets, catalogs, lists, parameters and formulas representing the model-data
c (master-data) and cycle-data (transactional-data).
c
c * Note that the "problem-data" (model-data and cycle-data) is converted to "matrix-data" in the "modeler" where it
c creates/generates the variables (columns), constraints (rows) and derivatives (Jacobian/Hessian) (and expressions).
rtnstat = IMPLrender(fact,ALL)
if (rtnstat == 0) then
logmessage = "<<< Render successful."
else
logmessage = "<<< Render unsuccessful."
end if
c "Model" the industrial/manufacturing optimization problem (IOP/MOP).
c
c Modeling the problem creates/generates the variables and constraints and any dependent sets, catalogs, lists and parameters
c necessary to model all of the physical (structural), procedural (operational), projectional and phenomenological details.
c In addition, the derivatives and expressions are also created or generated in the "modeler".
c First, model the sets, catalogs, lists and parameters ("setup").
rtnstat = IMPLmodelerv(fact,form,fit,filter,focus,filler,foreign,force=PARAMETER)
stop
end if
ftime = DCLOCK()
stime = ftime
write(*,*)"<<<"
write(*,*)"<<< Model (Sets, Lists, Parameters) time = ",dttime
ttime2 = dttime
c Second, model the variables ("specify").
rtnstat = IMPLmodelerv(fact,form,fit,filter,focus,filler,foreign,force=VARIABLE)
stop
end if
ftime = DCLOCK()

stime = ftime
write(*,*)"<<< Model (Variables) time = ",dttime
ttime2 = ttime2 + dttime
c If this is a "warm-start" then open the variables' results data file if it exists and read in the result values
c for the "original" problem i.e., previous solution-data.
if ((fuse == WARMSTART$) .and. (fork /= SECQPE_SORVE$)) then
lun = INILUN
INQUIRE(UNIT=lun,OPENED=rtnstat)
do while (rtnstat)
lun = lun + 1
end do
open(UNIT=lun,
& FILE=TRIM(fact)//"_vv.dta",
& STATUS="OLD",
& ACCESS="SEQUENTIAL",
& ACTION="READ",
& FORM="FORMATTED",
& SHARED,
& IOSTAT=rtnstat)
allocate(rval(1:vvc))
do i = 1,vvc
read(lun,*)rval(i)
end do
close(lun)
rtnstat = IMPLrevise2(VARIABLE,vvc,1,0,rval(1:vvc))
deallocate(rval)
end if
end if
c Third (last), model the constraints ("specificate/simulate/stipulate").
c
c * Note that only in the constraints section of the modeling do we require the formulas.
rtnstat = IMPLmodelerc(fact,form,fit,filter,focus,filler,foreign,force=CONSTRAINT)
stop
write(*,*)"<<< %ERROR% - Invalid input."
stop
end if
if ((fuse == WARMSTART$) .and. (fork == SECQPE_SORVE$)) then
lun = INILUN
do while (rtnstat)
lun = lun + 1
end do
open(UNIT=lun,
& STATUS="OLD",
& ACTION="READ",
& FORM="FORMATTED",
& SHARED,
& IOSTAT=rtnstat)
do i = 1,vvc
read(lun,*)rval(i)
end do
close(lun)
rtnstat = IMPLrevise2(VARIABLE,vvc,1,0,rval(1:vvc))
deallocate(rval)
end if
end if
ftime = DCLOCK()
stime = ftime
write(*,*)"<<< Model (Constraints) time = ",dttime
ttime2 = ttime2 + dttime
write(*,*)"<<< Model time = ",ttime2
write(*,*)"<<<"
rtnstat = IMPLreassure()
if (rtnstat /= 0) then
write(*,*)"<<< %ERROR% - Memory violation for resource-entity type = ",TRIM(IMPLreason(rtnstat))
stop
end if
logmessage = "<<< Reassure successful."
c "Interface" the superstructure data i.e., export or write out the *.io, *.oi and *.iod files.
face = INOUT$
rtnstat = IMPLinterfacere(fact,form,fit,filter,focus,face,factor,fob,frames)

face = OUTIN$
face = INOUTDATA$
c "Presolve" and "solve" the industrial/manufacturing optimization problem (IOP/MOP).
rtnstat = IMPLpresolver(fact,form,fit,filter,focus,factorizer,fork,fresh,flashback,feedback)
stop
write(*,*)"<<< %ERROR% - Invalid input."
stop
write(*,*)"<<< %ERROR% - Not all variables/constraints converged - see *.tdt file."
flag = 1
rtnstat = IMPLstatics(fact,CONTOL,flag)
end if
setting = "ECLOSURE2"
write(*,*)"<<<"
write(*,*)"<<< Solve closure = ",IMPLretrieveSTATISTIC(setting)
write(*,*)"<<<"
ftime = DCLOCK()
stime = ftime
write(*,*)"<<<"
write(*,*)"<<< Solve time = ",dttime
write(*,*)"<<<"
c Rerun/restart/repeat IMPL using different random-seeds by restoring the model-data and cycle-data from the
c binary/unformatted *.bdt file (see the render() routine which writes this file).
c
c This is useful for non-convex nonlinear (quality) that exhibit several local optima.
setting = "RANDSEED"
rseed = INT(IMPLretrieveSETTING(setting))
do i = 1,frequency
rtnstat = IMPLrefresh(ALL)
rtnstat = IMPLrestore(fact,ALL)
rtnstat = IMPLreceiveSETTING(setting,DBLE(rseed+i))
end do
c Save the solution-data ("original" problem) for the next run or execution if the warm-start is activated.
if (fuse == WARMSTART$) then
lun = INILUN
do while (rtnstat)
lun = lun + 1
end do
open(UNIT=lun,
& STATUS="UNKNOWN",
& ACTION="WRITE",
& FORM="FORMATTED",
& SHARED,
& IOSTAT=rtnstat)
call IMPLreview2(VARIABLE,vvc,1,0,rval(1:vvc))
do i = 1,vvc
write(lun,*)rval(i)
end do
deallocate(rval)
close(lun)
end if
end if
c "Interface" the solution data i.e., export or write out the solution(s) in the *.exl file and using
c *.oml file then "output" the selected solution variables results to the files specified.
frames = CHAR(0)
face = EXPORT$
face = OUTPUT$
ftime = DCLOCK()
stime = ftime
write(*,*)"<<<"

write(*,*)"<<< Export time = ",dttime
write(*,*)"<<<"
ftime = DCLOCK()
stime = ftime
write(*,*)"<<<"
write(*,*)"<<< Serialization time = ",dttime
write(*,*)"<<<"
c "Show" the various data for problem-solving, debugging and troubleshooting.
if (BTEST(fanfare,SHOWREPORT$)) then
rtnstat = IMPLreport(fact)
logmessage = "<<< Write report successful."
end if
if (BTEST(fanfare,SHOWSUMMARY$)) then
rtnstat = IMPLsummary(fact)
logmessage = "<<< Write summary successful."
end if
if (BTEST(fanfare,SHOWSTATICS$)) then
flag = 1
rtnstat = IMPLstatics(fact,CONTOL,flag)
logmessage = "<<< Write statics successful."
end if
if (BTEST(fanfare,SHOWSLACKNESS$)) then
if (filter == QUALITY$) then
ptr_IMPLmodelerc = LOC(IMPLmodelerc)
rtnstat = IMPLslackness(fact,ptr_IMPLmodelerc,0d+0)
logmessage = "<<< Write slackness successful."
end if
end if
if (BTEST(fanfare,SHOWSCALINGS$)) then
if ((form == SPARSIC$) .and. (filter == QUALITY$)) then
rtnstat = IMPLscalings(fact)
logmessage = "<<< Write scalings successful."
end if
end if
if (BTEST(fanfare,SHOWSERIESSET$)) then
rtnstat = IMPLwriteall(fact,SERIESSET,0,0)
logmessage = "<<< Writeall series-set successful."
end if
if (BTEST(fanfare,SHOWSIMPLESET$)) then
rtnstat = IMPLwriteall(fact,SIMPLESET,0,0)
logmessage = "<<< Writeall simple-set successful."
end if
if (BTEST(fanfare,SHOWSYMBOLSET$)) then
rtnstat = IMPLwriteall(fact,SYMBOLSET,0,0)
logmessage = "<<< Writeall symbol-set successful."
end if
if (BTEST(fanfare,SHOWCATALOG$)) then
rtnstat = IMPLwriteall(fact,CATALOG,0,0)
logmessage = "<<< Writeall catalog successful."
end if
if (BTEST(fanfare,SHOWLIST$)) then
rtnstat = IMPLwriteall(fact,LIST,0,0)
logmessage = "<<< Writeall list successful."
end if
if (BTEST(fanfare,SHOWPARAMETER$)) then
rtnstat = IMPLwriteall(fact,PARAMETER,0,0)
logmessage = "<<< Writeall parameter successful."
end if
if (BTEST(fanfare,SHOWVARIABLE$)) then
rtnstat = IMPLwriteall(fact,VARIABLE,0,0)
logmessage = "<<< Writeall variable successful."
end if
if (BTEST(fanfare,SHOWCONSTRAINT$)) then
rtnstat = IMPLwriteall(fact,CONSTRAINT,0,0)
logmessage = "<<< Writeall constraint successful."
end if
if (BTEST(fanfare,SHOWFORMULA$)) then
rtnstat = IMPLwriteall(fact,FORMULA,0,0)
logmessage = "<<< Writeall formula successful."
end if
c Show the model sensitivity with sparsity-pattern.
if (BTEST(fanfare,SHOWDERIVATIVE$)) then

flag = WRITESENSITIVITY
rtnstat = IMPLwritesensitivity(fact,ptr_IMPLmodelerc,flag)
logmessage = "<<< Write sensitivity successful."
end if
c Show model symbology with all variables and constraints as infix expressions.
if (BTEST(fanfare,SHOWEXPRESSION$)) then
if ((form == SYMBOLIC$) .or.
& (filter == QUANTITY$) .or. (filter == LOGISTICS$)) then
flag = WRITESYMBOLOGY
rtnstat = IMPLwritesymbology(fact,ptr_IMPLmodelerc,flag)
logmessage = "<<< Write symbology successful."
end if
end if
c Show model sensability matrices i.e., A, B, C, Q, xm and z data files.
if ((focus == ESTIMATION$) .and. (fork /= SVVE$) .and. (fork /= SSDE$)) then
flag=0
rtnstat = IMPLwritesplitability(fact,ptr_IMPLmodelerc,flag)
logmessage = "<<< Writeall splitability successful."
end if
c Deallocate global internal memory.
rtnstat = IMPLrelease(ALL)
logmessage = "<<< Release successful."
else
logmessage = "<<< Release unsuccessful."
end if
ftime = DCLOCK()
stime = ftime
write(*,*)"<<<"
write(*,*)"<<< Total time = ",ttime
write(*,*)"<<<"
c Finally, completely setup the problem again and show the symbolic representation of the problem if the form is "sparsic"
c and the filter is "quality".
if (BTEST(fanfare,SHOWEXPRESSION$)) then
if ((form == SPARSIC$) .and. (filter == QUALITY$)) then
rtnstat = IMPLreserve(fact,ALL)
rtnstat = IMPLrestore(fact,ALL)
form = SYMBOLIC$
fork = SOLVERLESS$
fresh = FIRSTSESSION$
flag = WRITESYMBOLOGY
rtnstat = IMPLwritesymbology(fact,ptr_IMPLmodelerc,flag)
rtnstat = IMPLrelease(ALL)
logmessage = "<<< Write symbology successful."
end if
end if
end program IMPL

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