Tight Bounds for Sorting Under Partial Information

Sorting has a natural generalization where the input consists of: (1) a ground set $X$ of size $n$, (2) a partial oracle $OP$ specifying some fixed partial order $P$ on $X$ and (3) a linear oracle $OL$ specifying a linear order $L$ that extends $P$. The goal is to recover the linear order $L$ on $X$ using the fewest number of linear oracle queries. In this problem, we measure algorithmic complexity through three metrics: oracle queries to $OL$, oracle queries to $OP$, and the time spent. Any algorithm requires worst-case $\log2 e(P)$ linear oracle queries to recover the linear order on $X$. Kahn and Saks presented the first algorithm that uses $\Theta(\log e(P))$ linear oracle queries (using $O(n^2)$ partial oracle queries and exponential time). The state-of-the-art for the general problem is by Cardinal, Fiorini, Joret, Jungers and Munro who at STOC'10 manage to separate the linear and partial oracle queries into a preprocessing and query phase. They can preprocess $P$ using $O(n^2)$ partial oracle queries and $O(n^{2.5})$ time. Then, given $OL$, they uncover the linear order on $X$ in $\Theta(\log e(P))$ linear oracle queries and $O(n + \log e(P))$ time -- which is worst-case optimal in the number of linear oracle queries but not in the time spent. For $c \geq 1$, our algorithm can preprocess $OP$ using $O(n^{1 + \frac{1}{c}})$ queries and time. Given $OL$, we uncover $L$ using $\Theta(c \log e(P))$ queries and time. We show a matching lower bound, as there exist positive constants $(\alpha, \beta)$ where for any constant $c \geq 1$, any algorithm that uses at most $\alpha \cdot n^{1 + \frac{1}{c}}$ preprocessing must use worst-case at least $\beta \cdot c \log e(P)$ linear oracle queries. Thus, we solve the problem of sorting under partial information through an algorithm that is asymptotically tight across all three metrics.