Stefan asks: << Suppose we have the m different vectors x_1, x_2, ..., x_m in the R^n. No two vectors are the same and no vector is the zero vector. Furthermore we calculate for every 1<=i<j<=m the normal dot product x_i.x_j How many different numbers are we at least going to get, regardless of the choice of x_1, x_2, ..., x_m? . . . [W]hat if m>n? Is anything known about this problem? Any ideas, results, bounds?

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Also of course if m = n+1, the vectors can be the n+1 vertices of a regular n-simplex centered at the origin, having only 1 value of x_i*x_j for any i <>j. I find the problem more natural if the vectors are assumed to be of constant length. Then the question is essentially: "Given m distinct points on the surface of the unit sphere in R^n, what is the smallest size that the set of their mutual distances can be?" A natural generalization of this version would be to consider a set of m points that all lie in any other highly symmetric space S -- such as S = the n-torus (or S = any homogeneous space), and again ask how small the set of distinct distances can be. Very nice geometric questions here. It may be possible -- and fun -- to find good answers numerically by taking any starting configuration of the m points on the metric space S, and sum over the (m(m-1)/2)(m(m-1)/2 - 1)/2 pairs of distinct ordered pairs (i,j),(i',j') for 1 <= i < j <= m and 1 <= i' < j' <= m. Then minimize this sum via some robust minimization algorithm, like conjugate gradient, using many starting configurations.) The spheres S^2 and S^3, the projective spaces P^2 and P^3, and the tori T^2 and T^3 would be especially interesting to work on, as well as complex projective spaces like CP^2 and compact connected Lie groups like SO(4). --Dan