bco
tron
AMPL
neos_tron
sarich@mcs.anl.gov
TRON was developed by
Chih-Jen Lin
and
Jorge J. More'.
Using the NEOS Server for TRON
The user must submit a model in
AMPL format to solve a
bound constrained optimization problem. Examples of models in AMPL format
can be found in the netlib
collection.
The model is specified by a model file, and optionally, a data file and a
commands file. If the command file is specified it must contain the AMPL
solve command.
The commands file can contain any AMPL command or set options for TRON.
Printing directed to standard out is returned to the user with the output.
]]>
http://www-unix.mcs.anl.gov/~more/tron/index.html
model
ampl.mod
Model File
Enter the location of the ampl model (local file)
data
ampl.dat
Data File
Enter the location of the ampl data file (local file)
commands
ampl.com
Commands File
Enter the location of the ampl commands file (local file)
comments
comments
Comments
newton.mcs.anl.gov
neosotc
3
3600
100000
100
long
param n0 := 11;
param n := n0+1;
set X := {0..n};
set Y := {0..n};
set X0 := {0..n0};
set Y0 := {0..n0};
param xsize := 2;
param ysize := 2;
param hx := xsize/n;
param hy := ysize/n;
param gamma0 {x in X} := 1.5*x*(n-x)/(n/2)^2;
param gamma1 {y in Y} := 2*y*(n-y)/(n/2)^2;
param gamma2 {x in X} := 4*x*(n-x)/(n/2)^2;
param gamma3 {y in Y} := 2*y*(n-y)/(n/2)^2;
var z {X, Y};
minimize area:
(hx*hy/2)*
sum {x in X0, y in Y0}
(
sqrt(1 + ((z[x+1,y] - z[x,y])/hx)^2 + ((z[x,y+1] - z[x,y])/hx)^2)
+
sqrt(1 + ((z[x+1,y+1] - z[x,y+1])/hx)^2 + ((z[x+1,y+1] - z[x+1,y])/hx)^2)
);
subject to bndcnd0 {x in X}: z[x,0] = gamma0[x];
subject to bndcnd1 {y in Y}: z[n,y] = gamma1[y];
subject to bndcnd2 {x in X}: z[x,n] = gamma2[x];
subject to bndcnd3 {y in Y}: z[0,y] = gamma3[y];
solve;
display z;
Comment here
0 integer;
param M > 0 integer;
set I := 1 .. N;
set J := 1 .. M;
param y {j in J};
param t {j in J} := 10 * (j - 1);
#var x {i in I} >= -10, <= 10;
var x {i in I} >= -2, <= 10;
minimize ssq:
sum {j in J}
(y[j] - (x[1] + x[2]*exp(-t[j]*x[4]) + x[3]*exp(-t[j]*x[5])))^2;
data;
param N := 5;
param M := 33;
param y :=
1 .844 7 .881 13 .685 19 .538 25 .457 31 .414
2 .908 8 .85 14 .658 20 .522 26 .448 32 .411
3 .932 9 .818 15 .628 21 .506 27 .438 33 .406
4 .936 10 .784 16 .603 22 .49 28 .431
5 .925 11 .751 17 .58 23 .478 29 .424
6 .908 12 .718 18 .558 24 .467 30 .42
;
var x := # initial guess
1 .5
2 1.5
3 -1
4 .01
5 .02
;
]]>
0 integer default 2; # space dimension
set D := 1..d;
param N > 0 integer default 10; # number of atoms
set I := {1..N};
set P := {i in I, j in 1..i-1}; # pairs of atoms
var x {i in I, D} default i;
var r {(i,j) in P} = # distance separating atoms i and j
sqrt( sum{k in D} (x[i,k] - x[j,k])^2 );
minimize energy:
sum {(i,j) in P} (r[i,j]^-12 - 2*r[i,j]^-6);
]]>