given a matrix of distances between points is there an algorithm for determining a set of n-dimensional points that has these distances? (or at least minimises the error)
sort of like a n-dimensional version of the turnpike problem.
The best I can come up with is using multidimensional scaling.
You are on the right track with multi-dimensional scaling (MDS), but MDS is impractical for large datasets, as its time complexity is quadratic in the number of points. You may want to look at FastMap, which has linear time complexity and is better suited to indexing. See:
Christos Faloutsos and King-Ip Lin: "FastMap: a Fast Algorithm for Indexing, Data-Mining and Visualization of Traditional and Multimedia Datasets, in Proc. SIGMOD, 1995, doi:10.1145/223784.223812
You can "cheat" and use an iterative numerical method for this. Take all of the points to be in some "random" positions initially, and then loop through them, moving them away from each other proportionally to the required distance. This will prefer some points, but taking an average of the moves before applying them, then applying the average will remove this problem. This is an O(n²) algorithm, but very simple to implement and understand. In the 2-d example below the error is << 10%, though it may not behave so well if the distances given are unrealistic.
C++ Example:
#include <conio.h>
#include <math.h>
#include <stdio.h>
#include <stdlib.h>
#define DAMPING_FACTOR 0.99f
class point
{
public:
float x;
float y;
public:
point() : x(0), y(0) {}
};
// symmetric matrix with distances
float matrix[5][5] = {
{ 0.0f, 4.5f, 1.5f, 2.0f, 4.0f },
{ 4.5f, 0.0f, 4.0f, 3.0f, 3.5f },
{ 1.5f, 4.0f, 0.0f, 1.0f, 5.0f },
{ 2.0f, 3.0f, 1.0f, 0.0f, 4.5f },
{ 4.0f, 3.5f, 5.0f, 4.5f, 0.0f }
};
int main(int argc, char** argv)
{
point p[5];
for(unsigned int i = 0; i < 5; ++i)
{
p[i].x = (float)(rand()%100)*0.1f;
p[i].y = (float)(rand()%100)*0.1f;
}
// do 1000 iterations
float dx = 0.0f, dy = 0.0f, d = 0.0f;
float xmoves[5], ymoves[5];
for(unsigned int c = 0; c < 1000; ++c)
{
for(unsigned int i = 0; i < 5; ++i) xmoves[i] = ymoves[i] = 0.0f;
// iterate across each point x each point to work out the results of all of the constraints in the matrix
// collect moves together which are slightly less than enough (DAMPING_FACTOR) to correct half the distance between each pair of points
for(unsigned int i = 0; i < 5; ++i)
for(unsigned int j = 0; j < 5; ++j)
{
if(i==j) continue;
dx = p[i].x - p[j].x;
dy = p[i].y - p[j].y;
d = sqrt(dx*dx + dy*dy);
dx /= d;
dy /= d;
d = (d - matrix[i][j])*DAMPING_FACTOR*0.5f*0.2f;
xmoves[i] -= d*dx;
ymoves[i] -= d*dy;
xmoves[j] += d*dx;
ymoves[j] += d*dy;
}
// apply all at once
for(unsigned int i = 0; i < 5; ++i)
{
p[i].x += xmoves[i];
p[i].y += ymoves[i];
}
}
// output results
printf("Result:\r\n");
for(unsigned int i = 0; i < 5; ++i)
{
for(unsigned int j = 0; j < 5; ++j)
{
dx = p[i].x - p[j].x;
dy = p[i].y - p[j].y;
printf("%f ", sqrt(dx*dx + dy*dy));
}
printf("\r\n");
}
printf("\r\nDesired:\r\n");
for(unsigned int i = 0; i < 5; ++i)
{
for(unsigned int j = 0; j < 5; ++j)
{
printf("%f ", matrix[i][j]);
}
printf("\r\n");
}
printf("Absolute difference:\r\n");
for(unsigned int i = 0; i < 5; ++i)
{
for(unsigned int j = 0; j < 5; ++j)
{
dx = p[i].x - p[j].x;
dy = p[i].y - p[j].y;
printf("%f ", abs(sqrt(dx*dx + dy*dy) - matrix[i][j]));
}
printf("\r\n");
}
printf("Press any key to continue...");
while(!_kbhit());
return 0;
}
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