> /*
> Program 2
> */
>
> #include "tnt.h"
> #include "jama_cholesky.h"
> #include "jama_lu.h"
>
> using namespace JAMA;
> using namespace TNT;
> using namespace std;
>
> int main()
> {
> Array2D C(5,5);
> Array1D c(5);
> Array2D V(5,5);
> Array1D v(5);
> Array1D Xc;
> Array1D Xv;
> Stopwatch Q;
> int i;
>
> C[0][0]=2000; C[1][0]=704.8; C[2][0]=695.6;C[3][0]=
> 466.4; C[4][0]=1;
> C[0][1]=704.8; C[1][1]=2000;C[2][1]=689.4;C[3][1]=
> 461.2; C[4][1]=1;
> C[0][2]=695.6; C[1][2]=689.4; C[2][2]=2000; C[3][2]=
> 1285.8; C[4][2]=1;
> C[0][3]=466.4; C[1][3]=461.2; C[2][3]=1285.8;
> C[3][3]=2000; C[4][3]=1;
> C[0][4]=1; C[1][4]=1; C[2][4]=1;C[3][4]=1;
> C[4][4]=0;
>
> c[0]=908.7;
> c[1]=831.8;
> c[2]=1507.2;
> c[3]=973.6;
> c[4]=1;
>
> V[0][0]=0; V[1][0]=1295.2; V[2][0]=1304.4;V[3][0]=
> 1533.6; V[4][0]=1;
> V[0][1]=1295.2; V[1][1]=0;V[2][1]=1310.6;V[3][1]=
> 1538.8; V[4][1]=1;
> V[0][2]=1304.4; V[1][2]=1310.6; V[2][2]=0; V[3][2]=
> 714.2; V[4][2]=1;
> V[0][3]=1533.6; V[1][3]=1538.8; V[2][3]=714.2;
> V[3][3]=0; V[4][3]=1;
> V[0][4]=1; V[1][4]=1;V[2][4]=1;
> V[3][4]=1; V[4][4]=0;
>
>
> v[0]=1091.3;
> v[1]=1168.2;
> v[2]=492.8;
> v[3]=1026.4;
> v[4]=1;
>
>
> //To check results
> //LU with Covariance
> {LU lu(C);
> Xc=lu.solve(c);
> cout << "LU con C: " << Xc <
> //LU with Varigram
> {LU lu(V);
> Xv=lu.solve(v);
> cout << "LU con V: " << Xv <
>
> //To compare speed
>
> //LU with Covariance
> Q.start();
> for(i=1; i<=10; ++i)
> {
> LU lu(C);
> Xc=lu.solve(c);
> }
> Q.stop();
> cout << "Tiempo de LU con C: " << Q.read() <
> //LU with Varigram
> Q.start();
> for(i=1; i<=10; ++i)
> {
> LU lu(V);
> Xv=lu.solve(v);
> }
> Q.stop();
> cout << "Tiempo de LU con V: " << Q.read() <
> return 0;
> }
>
> - Original Message -
> *From:* Isobel Clark <[EMAIL PROTECTED]>
> *To:* Adrian Martínez Vargas <[EMAIL PROTECTED]> ; ai-geostats@jrc.it
> *Sent:* Friday, March 28, 2008 6:31 PM
> *Subject:* AI-GEOSTATS: Re: Numerical method to solve kriging equations
>
>
> Adrian
>
> It is a common misconception that using the covariance (total sill -
> semi-variogram) rather than the semi-variogram brings more robust solutions.
> You get exactly the same answer either way since one is just a constant
> minus the other.
>
> You can avoid solution problems by simple pivoting or by putting the
> condition equation first -- sum of weights equals 1.
>
> If you look at the details of the solution, you generally only have to
> pivot the first equation to remove the diagonal zeroes.
>
> Isobel
> http://courses.kriging.com
>
>
> *Adrian Martínez Vargas <[EMAIL PROTECTED]>* wrote:
>
> What about to produce "pseudo covariance" to replace kriging matrix in
> term of variogram to make more efficient the numerical solution of the
> system? The ceros in the matrix diagonal are a problem in robustness and
> efficiency!
>
> Some one knows how to implement something like that? Papers/books can be
> useful!
>
> - Original Message -
> *From:* Adrian Martínez Vargas <[EMAIL PROTECTED]>
> *To:* ai-geostats@jrc.it
> *Sent:* Friday, March 28, 2008 5:23 PM
> *Subject:* Numerical method to solve kriging equations
>
>
> Hello dear list
>
> What numerical method give faster and robust solution to kriging
> equations. What to us as C++ library (for example TNT and JAMA?). It is
> usual to use cholesky in the case of simple kriging.
>
> I will appreciate your advice and experiences.
>
> Best regards
> Dr. Adrian Martínez Vargas
> Revista Minería y Geología
> ISMM, Las Coloradas, s/n
> Moa, Holguín,
> Cuba
> CP. 83329
> http://www.ismm.edu.cu/revistamg/index.htm
>
>
>
>
