Creating a 2D map of objects given a sparse matrix of pairwise distances I have a set of points on the two-dimensional plane, but their locations are not given to me.  I am given the distance between some pairs of the points.  However, I only know these differences for some pairs: I don't have every pairwise distance between the objects. I would like an algorithm to output the locations of the points.

The complication is that there could be some errors in the distances provided to me, so I want the algorithm to output a location for each point such that the pairwise distances between those locations match the provided distances as closely as possible.

Could anyone point me towards a method of computing a placement in  2-dimensional Euclidean space that would best solve this problem? 

It would be nice to be able to provide hard constraints as well. For instance, I might be given that $C$ is exactly at the point $(1,2)$, or that all of the objects must fit in a $10 \times 10$ square.  However, this is not strictly necessary: an algorithm that does not support hard constraints would still be interesting.
This is known as "graph layout" or "graph drawing" (practical methods) or "graph embedding" (theoretical interest).  The relevant techniques are more closely associated with computational geometry and mathematical optimization, not machine learning.   See this Wikipedia article and Recovering a point embedding from a graph with edges weighted by point distance and Efficient algorithm to fulfil a set of coordinate constraints for an overview of some standard techniques.  Pairwise comparisons with confidence might also have some helpful techniques (it is focused on the 1D case, not the 2D case, but it might be possible to generalize Yuval's answer to the 2D case as well).