himmel16.gms : Area of Hexagon Test Problem
The physical problem is to maximize the area of a hexagon in which the
diameter must be less than or equal to one. The formulation in Himmelblau
is different from the one given here, because certain fixed variables
have been eliminated. However, the formulation given here is more natural
and easier to understand. It makes use of the fact that one vertex is fixed
at the origin and uses the vector scalar product to calculate the areas of
all triangles originating from the origin. All terms in x(1) and y(1) thus
vanish when the algebraic expression is simplified.
The problem appears many other places, e.g. as example 108 in W. Hock and
K. Schittkowski: Test Examples for Nonlinear Programming Codes, Lecture
Notes in Economics and Mathematical Systems, 187, Springer Verlag, 1981,
and as the test example in P. E. Gill, W. Murray, M. A. Saunders, and
M. Wright: User's Guide for SOL/NPSOL: A FORTRAN Package for Nonlinear
Programming, Tech. Rep. 83-12, Dept. of Operation Research, Stanford
University.
Reference:
- Himmelblau, D M, Problem Number 16. In Applied Nonlinear Programming. Mc Graw Hill, New York, 1972.
Small Model of Type: NLP
<![CDATA[
$Title Area of Hexagon Test Problem (HIMMEL16,SEQ=36)
$Ontext
The physical problem is to maximize the area of a hexagon in which the
diameter must be less than or equal to one. The formulation in Himmelblau
is different from the one given here, because certain fixed variables
have been eliminated. However, the formulation given here is more natural
and easier to understand. It makes use of the fact that one vertex is fixed
at the origin and uses the vector scalar product to calculate the areas of
all triangles originating from the origin. All terms in x(1) and y(1) thus
vanish when the algebraic expression is simplified.
The problem appears many other places, e.g. as example 108 in W. Hock and
K. Schittkowski: Test Examples for Nonlinear Programming Codes, Lecture
Notes in Economics and Mathematical Systems, 187, Springer Verlag, 1981,
and as the test example in P. E. Gill, W. Murray, M. A. Saunders, and
M. Wright: User's Guide for SOL/NPSOL: A FORTRAN Package for Nonlinear
Programming, Tech. Rep. 83-12, Dept. of Operation Research, Stanford
University.
Himmelblau, D M, Problem Number 16. In Applied Nonlinear Programming.
Mc Graw Hill, New York, 1972.
$Offtext
Set i indices for the 6 points /1*6/;
Alias (i,j);
Variable x(i) x-coordinates of the points
y(i) y-coordinates of the points
area(i) area of the i'th triangle ( 0 -> p(i) -> p(i++1) -> 0
totarea total area of the hexagon
Equations areadef(i) area definition for triangle i.
maxdist(i,j) maximal distance between i and j
obj1 first definition of objective
obj2 second definition of objective;
maxdist(i,j)$(ord(i) lt ord(j)).. sqr(x(i)-x(j))+sqr(y(i)-y(j)) =l= 1;
areadef(i).. area(i) =e= 0.5*(x(i)*y(i++1)-y(i)*x(i++1)) ;
obj1.. totarea =e= 0.5*sum(i,x(i)*y(i++1)-y(i)*x(i++1));
obj2.. totarea =e= sum(i,area(i));
Model small /maxdist,obj1/
large /maxdist,obj2,areadef/ ;
*
* initial conditions
*
x.fx("1") = 0; y.fx("1") = 0; y.fx("2") = 0;
x.l("2") = 0.5; x.l("3") = 0.5; x.l("4") = 0.5;
x.l("5") = 0; x.l("6") = 0;
y.l("3") = 0.4; y.l("4") = 0.8; y.l("5") = 0.8;
y.l("6") = 0.4;
Solve large using nlp maximizing totarea;
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