Hi help-glpk team:
I'm doing a research about a consensus and a got a question:
I have as parameter a matrix A of size mxn like this
| y1 y2 y3
---- |------------------
x1 | 1 2 2
x2 | 1 2 1
x3 | 2 3 2
x4 | 3 2 3
where every element in the matrix represent an label A=1 , B=2 and C=2
and when I resolve my model I get a vector solution Z of size mx1 like this:
| Z[i]
----|--------
x1 | 2
x2 | 1
x3 | 2
x4 | 3
where every point represent the minimum distance with respect cell aij in
matrix A
my objective function is:
min sum{i in I, j in J } | aij - zi | (in my model, this objective
function was already linearized )
now suppose that I have an number r o matrices A e.g. A1, A2, A3 ... At
and I get a solution z for every matrix like above.
then I need evaluate every solution z in every Matrix Ar , r = 1,...,t
except the matrix where the solution came from. i.e. now I have matrix A
and solution z and I don't need optimize only evaluate solutions found in
previous matrices
Can you help me with this issue?
Thanks in advance
Best regards
--
*RAFAEL TORRES ESCOBAR*
Alumno Maestría en Ingeniería de Sistemas
Facultad de Ingeniería Mecánica y Eléctrica
UNIVERSIDAD AUTÓNOMA DE NUEVO LEÓN.
México.
*http://pisis.fime.uanl.mx/students/rafael_torres.html*
*
**[email protected]*
*
***Programa de Posgrado en Ingeniería de Sistemas
F IME - U A N L
Cd. Universitaria
San Nicolás de los Garza, Nuevo León C.P. 66450
MÉXICO*
http://pisis.fime.uanl.mx/
http://pisis.fime.uanl.mx/coordenadas.html
[email protected]
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GBY
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