Use AMPL's shell command to invoke the editor you want to use. For example,
if your editor is called edit and your file is diet2a.dat,
give the command
shell "edit diet2a.dat";
The editor temporarily takes over the screen or current window. When you quit
the editor, the AMPL prompt reappears, and all models, data and options are as
before.
When you have returned from the editor, AMPL cannot tell which files you have
changed! Thus to use a changed file, you must reset and read it again. The
simplest approach is to reset and re-read everything, as in this example:
ampl: shell "edit diet.mod";
ampl: reset;
ampl: model diet.mod;
ampl: data diet2a.dat;
See our discussion of new
reset features for ways to partially reset the model and data.
In a multiple-window environment (such as X on Unix workstations or Microsoft
Windows on PCs) it's easier to edit files in their own windows, separate from
the windows where you're running AMPL. You still need to reset and re-read after
changing a file, though.
If you use a graphical
interface to AMPL then you can create editing windows within the AMPL
environment. The interface does know when you have changed a file, and it may
carry out the reset and re-read operations automatically. To learn more, consult
the instructions for the interface that you are using.
Use AMPL's reset data command to resample from all of the
random-valued functions in the model. For example, in model steel4.mod
from the AMPL book, suppose that
parameter avail is changed so that its value is given by a random
function:
param avail_mean {STAGE} >= 0;
param avail_variance {STAGE} >= 0;
param avail {s in STAGE} :=
Normal (avail_mean[s], avail_variance[s]);
with corresponding data
param: avail_mean avail_variance :=
reheat 35 5
roll 40 2 ;
Then AMPL will take new samples from the Normal distribution after each
reset data:
ampl: model steel4.mod;
ampl: data steel4.dat;
ampl: solve;
MINOS 5.4: optimal solution found.
3 iterations, objective 187632.2489
ampl: display avail;
reheat 32.3504
roll 43.038 ;
ampl: reset data avail;
ampl: solve;
MINOS 5.4: optimal solution found.
4 iterations, objective 158882.901
ampl: display avail;
reheat 32.0306
roll 32.6855 ;
Using this feature together with one of AMPL's new looping commands, you can
automatically solve a series of random realizations from the model, and
summarize the results:
model steel4.mod;
data steel4.dat;
param nruns := 5;
param optvalue {1..nruns};
for {k in 1..nruns} {
reset data avail;
solve;
let optvalue[k] := total_profit;
}
display (sum {k in 1..nruns} optvalue[k]) / nruns;
If you use reset rather than reset data, then
AMPL's random number generator is reset, and the values of avail repeat
from the beginning. To get a different sequence of random numbers from the
generator, you must change the random number seed; give the command
option randseed n -- where n is a positive
integer -- or option randseed 0 to have AMPL choose a
seed based on the system clock.
The check conditions are evaluated each time that AMPL generates (or
re-generates) an instance of your model. Normally the generation of an instance
is triggered by a solve command, but a few other commands such as
write and solution can have the same effect.
You can force all check statements to be evaluated immediately by
typing the command
check;
or by inserting this command into an AMPL script at the point where you want
the checking to occur.
You can use AMPL's write command to create a file that contains a
representation of your linear or integer program in a standard format known as
MPS form. To write an MPS-form file for the diet LP from Chapter 2 of the
AMPL book, for example, you could
proceed as follows:
ampl: model diet.mod;
ampl: data diet2a.dat;
ampl: option auxfiles rc;
ampl: write mdiet2;
AMPL interprets "write m..." as indicating that you want to write an
MPS file, and creates the filename by appending ".mps" to the letters
after the "m". Thus our example creates a file named diet2.mps. The format of this
file is explained in the Linear Programming FAQ and in the manuals for many solvers.
Because MPS form limits the row (constraint or objective) and column
(variable) names to 8 characters, AMPL substitutes artificial names such as
R0001 and C0007. You can ask for supplementary files of the
true AMPL component names, by also resetting option auxfiles
rc; in the above example, you would get files diet2.row and diet2.col. The ordering of the
names in these files corresponds to their numbering in the MPS file. (For a more
detailed description of write and the auxfiles option, see
Sections A.13.5 and A.13.6 of the AMPL
book.)
An MPS file contains only the nonzero values that define one instance of your
model. Thus an MPS file generated by AMPL is mainly useful as input to solvers
that do not yet have a direct AMPL interface; because MPS form has been in use
for a longer time than any comparable format, it is recognized by more solvers
than any other file type. You may also find AMPL's MPS-file option useful for
generating new instances of test problems, for submission to libraries such as
netlib's lp/data.
If you want to encourage
people to use AMPL with your own solver, however, then you should consider hooking
your solver to AMPL by use of AMPL's file format, which takes less space,
can be processed faster, represents numbers more accurately, and applies to a
broader variety of optimization problems (especially nonlinear ones).
Several kinds of transformations
may be applied by AMPL before it writes out any representation of your problem.
Normally you need not be aware of these changes, because AMPL reverses them
after receiving the optimal solution from the solver. If you write an MPS file,
however, it will correspond to the transformed problem; its coefficients and
other values may have been changed, it may lack some of the variables and
constraints that you defined, or it may contain auxiliary variables and
constraints that AMPL added. To get a summary of AMPL's transformations, set
option show_stats 1. To force AMPL to write the MPS
file for your problem as stated, turn
off the transformations.
Representations of numbers in MPS form are limited to 12 characters. As a
consequence, the numbers in an MPS file may not have the full precision of the
numbers that were generated from your AMPL model and data. Usually these
precision discrepancies are inconsequential, but they do give rise to
inaccuracies in the optimal solution computed from the MPS file.
Although MPS form is regarded as a standard, it has no definitive statement.
As a result, there are a few special cases that are treated differently by
different solvers; examples include variables that have an upper bound of zero
and no lower bound, and integer variables declared without any bounds. AMPL's
version of MPS form is designed to avoid using these unresolved default options,
so that its MPS files can be compatible with as many solvers as possible.
AMPL's presolve phase attempts to transform your problem to an
equivalent one that is smaller and easier to solve. Presolve first removes
trivial model components, such as variables fixed at constant values and
constraints that express simple lower or upper bounds. Then it applies an
iterative procedure to tighten certain bounds on variables and constraints, with
the result that additional variables may be fixed and constraints may be
dropped. Many of these transformations are based on ideas first presented by
A.L. Brearly, G. Mitra and H.P. Williams, in ``Analysis of Mathematical
Programming Problems Prior to Applying the Simplex Algorithm,'' Mathematical
Programming 8 (1975) 54-83. See also Experience with a Primal Presolve
Algorithm for a detailed discussion of the implementation in AMPL.
If your model uses AMPL's notation for piecewise-linear terms in individual
variables, then AMPL transforms your problem's piecewise-linear expressions to
equivalent linear expressions, through the addition of auxiliary variables and
constraints. Chapter 14 of the AMPL
book describes the piecewise-linear notation in more detail. See also Expressing Special Structures in an
Algebraic Modeling Language for Mathematical Programming for further
discussion of how the transformation is carried out.
You can cause AMPL to eliminate certain variables from your problem, by
substituting expressions specified in var declarations or in certain
constraints. If the substitution comes from a constraint, then the constraint is
also eliminated. See Sections 13.2 and A.15 of the AMPL book for more information.
To suppress the presolve phase, set option presolve
0.
To suppress transformation of piecewise-linear terms, set option
pl_linearize 0.
To suppress elimination of variables by substitution from constraints, set
option substout 0. (This is the default setting.)
Substitutions specified within var declarations are always applied.
To see a summary of all transformations performed, set option
show_stats 1.
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