initial coordinates for nodes
matrix of desired distances between pairs of nodes
[default=null] if specified, G is a matrix of weights for goal terms between pairs of nodes. If G[i][j] > 1 and the separation between nodes i and j is greater than their ideal distance, then there is no contribution for this pair to the goal If G[i][j] <= 1 then it is used as a weighting on the contribution of the variance between ideal and actual separation between i and j to the goal function
matrix of desired distances between pairs of nodes
[default=null] if specified, G is a matrix of weights for goal terms between pairs of nodes. If G[i][j] > 1 and the separation between nodes i and j is greater than their ideal distance, then there is no contribution for this pair to the goal If G[i][j] <= 1 then it is used as a weighting on the contribution of the variance between ideal and actual separation between i and j to the goal function
Hessian Matrix
gradient vector
number of data-points / nodes / size of vectors/matrices
positions vector
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Uses a gradient descent approach to reduce a stress or p-stress goal function over a graph with specified ideal edge lengths or a square matrix of dissimilarities. The standard stress function over a graph nodes with position vectors x,y,z is (mathematica input): stress[x_,y_,z_,D_,w_]:=Sum[w[[i,j]] (length[x[[i]],y[[i]],z[[i]],x[[j]],y[[j]],z[[j]]]-d[[i,j]])^2,{i,Length[x]-1},{j,i+1,Length[x]}] where: D is a square matrix of ideal separations between nodes, w is matrix of weights for those separations length[x1_, y1_, z1_, x2_, y2_, z2_] = Sqrt[(x1 - x2)^2 + (y1 - y2)^2 + (z1 - z2)^2] below, we use wij = 1/(Dij^2)
Descent