Generalizing linear interpolation to posets Assume that I have an array $A$ of $n$ numerical values where some are known and some are unknown (with $A[0]$ and $A[n-1]$ assumed to be known). If I want to estimate an unknown value $A[i]$, a reasonable way is to perform linear interpolation: find the largest $j &lt; i$ such that $A[j]$ is known, find the smallest $k &gt; i$ such that $A[k]$ is known, and do a weighted average $A[j] := {k-i \over k-j}A[k] + {i-j \over k-j}A[j]$. In particular, this ensures that the following property (*) holds: 

$$\text{if } A[j] \leq A[k] \text{ then } A[j] \leq A[i] \leq A[k]$$

Now, you can see an array as a totally ordered structure, and wonder about what would happen in the case of a partial order. Assume that I have a DAG $G = (V, E)$ of $n$ nodes where every node $v \in V$ has a numerical value $\mu(v)$ where some values are known and some are unknown (with the values of the roots and leaves assumed to be known). If I want to estimate some unknown $\mu(v)$, it seems that I should interpolate it out of the values of the closest ancestors and descendents of $v$ whose values are known. The question is: what should we do, exactly? Is there a generalization of linear interpolation in this setting?

[Here is a possible choice: for every couple $(u, w)$ of ancestors and descendents of $v$, perform linear interpolation along all the possible chains between $u$, $v$ and $w$ to get a value for $v$, and average all the values over all chains and ancestor-descendent pairs to get your final estimate. Is this the correct way to do things? Can this estimation be computed more efficiently than with this definition? (i.e., could we do it in linear time in the size of the neighborhood of $v$ under consideration, rather than in a quadratic way?). Do we still have some variant of property (*)? (note that, even if you assume monotonicity, i.e. for each ancestor-descendent couple $(u, w)$ you assume $\mu(u) \leq \mu(w)$, then the estimation $\mu(v)$ obtained by the previous process may still violate monotonicity, i.e. $\mu(v) &gt; \mu(u)$ for some ancestor $u$ of $v$)]
In a recent paper, we propose such a scheme. The scheme is illustated in a specific crowdsourcing application setting, but the idea is fairly simple. We just see the order constraint $\mu(u) \leq \mu(v)$ of each DAG edge $(u, v)$ as a linear inequality constraint, we consider the admissible convex polytope of assignments to these linear inequalities, and we choose the center of mass of this polytope as our interpolation result.

The paper explains the scheme in more detail, includes an algorithm, computational hardness results, an approximation scheme that derives from existing work, a tractable case when the DAG is a tree, and a discussion of alternate schemes.