1257 lines
37 KiB
TeX
1257 lines
37 KiB
TeX
\chapter{Tree queries}
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\index{tree query}
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This chapter discusses techniques for
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processing queries related
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to subtrees and paths of a rooted tree.
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For example, such queries are:
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\begin{itemize}
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\item what is the $k$th ancestor of a node?
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\item what is the sum of values in the subtree of a node?
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\item what is the sum of values in a path between two nodes?
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\item what is the lowest common ancestor of two nodes?
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\end{itemize}
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\section{Finding ancestors}
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\index{ancestor}
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The $k$th \key{ancestor} of a node $x$ in a rooted tree
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is the node that we will reach if we move $k$
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levels up from $x$.
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Let $f(x,k)$ denote the $k$th ancestor of a node $x$
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(or $0$ if there is no such an ancestor).
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For example, in the following tree, $f(2,1)=1$, $f(8,2)=4$
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and $f(5,2)=0$.
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\begin{center}
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\begin{tikzpicture}[scale=0.9]
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\node[draw, circle] (1) at (0,3) {$1$};
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\node[draw, circle] (2) at (2,1) {$2$};
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\node[draw, circle] (3) at (-2,1) {$4$};
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\node[draw, circle] (4) at (0,1) {$5$};
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\node[draw, circle] (5) at (2,-1) {$6$};
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\node[draw, circle] (6) at (-3,-1) {$3$};
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\node[draw, circle] (7) at (-1,-1) {$7$};
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\node[draw, circle] (8) at (-1,-3) {$8$};
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\path[draw,thick,-] (1) -- (2);
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\path[draw,thick,-] (1) -- (3);
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\path[draw,thick,-] (1) -- (4);
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\path[draw,thick,-] (2) -- (5);
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\path[draw,thick,-] (3) -- (6);
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\path[draw,thick,-] (3) -- (7);
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\path[draw,thick,-] (7) -- (8);
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\path[draw=red,thick,->,line width=2pt] (8) edge [bend left] (3);
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\path[draw=red,thick,->,line width=2pt] (2) edge [bend right] (1);
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\end{tikzpicture}
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\end{center}
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An easy way to calculate the value of $f(x,k)$
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is to perform a sequence of $k$ moves in the tree.
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However, the time complexity of this method
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is $O(n)$, because the tree may contain
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a chain of $O(n)$ nodes.
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Fortunately, it turns out that
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using a technique similar to that
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used in Chapter 16.3, any value of $f(x,k)$
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can be efficiently calculated in $O(\log k)$ time
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after preprocessing.
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The idea is to precalculate all values $f(x,k)$
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where $k$ is a power of two.
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For example, the values for the above tree
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are as follows:
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\begin{center}
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\begin{tabular}{r|rrrrrrrrr}
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$x$ & 1 & 2 & 3 & 4 & 5 & 6 & 7 & 8 \\
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\hline
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$f(x,1)$ & 0 & 1 & 4 & 1 & 1 & 2 & 4 & 7 \\
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$f(x,2)$ & 0 & 0 & 1 & 0 & 0 & 1 & 1 & 4 \\
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$f(x,4)$ & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 \\
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$\cdots$ \\
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\end{tabular}
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\end{center}
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The preprocessing takes $O(n \log n)$ time,
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because each node can have at most $n$ ancestors.
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After this, any value of $f(x,k)$ can be calculated
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in $O(\log k)$ time by representing $k$
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as a sum where each term is a power of two.
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\section{Subtrees and paths}
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\index{tree traversal array}
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A \key{tree traversal array} contains the nodes of a rooted tree
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in the order in which a depth-first search
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from the root node visits them.
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For example, in the tree
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\begin{center}
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\begin{tikzpicture}[scale=0.9]
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\node[draw, circle] (1) at (0,3) {$1$};
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\node[draw, circle] (2) at (-3,1) {$2$};
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\node[draw, circle] (3) at (-1,1) {$3$};
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\node[draw, circle] (4) at (1,1) {$4$};
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\node[draw, circle] (5) at (3,1) {$5$};
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\node[draw, circle] (6) at (-3,-1) {$6$};
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\node[draw, circle] (7) at (-0.5,-1) {$7$};
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\node[draw, circle] (8) at (1,-1) {$8$};
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\node[draw, circle] (9) at (2.5,-1) {$9$};
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\path[draw,thick,-] (1) -- (2);
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\path[draw,thick,-] (1) -- (3);
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\path[draw,thick,-] (1) -- (4);
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\path[draw,thick,-] (1) -- (5);
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\path[draw,thick,-] (2) -- (6);
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\path[draw,thick,-] (4) -- (7);
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\path[draw,thick,-] (4) -- (8);
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\path[draw,thick,-] (4) -- (9);
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\end{tikzpicture}
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\end{center}
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a depth-first search proceeds as follows:
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\begin{center}
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\begin{tikzpicture}[scale=0.9]
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\node[draw, circle] (1) at (0,3) {$1$};
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\node[draw, circle] (2) at (-3,1) {$2$};
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\node[draw, circle] (3) at (-1,1) {$3$};
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\node[draw, circle] (4) at (1,1) {$4$};
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\node[draw, circle] (5) at (3,1) {$5$};
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\node[draw, circle] (6) at (-3,-1) {$6$};
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\node[draw, circle] (7) at (-0.5,-1) {$7$};
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\node[draw, circle] (8) at (1,-1) {$8$};
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\node[draw, circle] (9) at (2.5,-1) {$9$};
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\path[draw,thick,-] (1) -- (2);
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\path[draw,thick,-] (1) -- (3);
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\path[draw,thick,-] (1) -- (4);
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\path[draw,thick,-] (1) -- (5);
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\path[draw,thick,-] (2) -- (6);
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\path[draw,thick,-] (4) -- (7);
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\path[draw,thick,-] (4) -- (8);
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\path[draw,thick,-] (4) -- (9);
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\path[draw=red,thick,->,line width=2pt] (1) edge [bend right=15] (2);
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\path[draw=red,thick,->,line width=2pt] (2) edge [bend right=15] (6);
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\path[draw=red,thick,->,line width=2pt] (6) edge [bend right=15] (2);
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\path[draw=red,thick,->,line width=2pt] (2) edge [bend right=15] (1);
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\path[draw=red,thick,->,line width=2pt] (1) edge [bend right=15] (3);
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\path[draw=red,thick,->,line width=2pt] (3) edge [bend right=15] (1);
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\path[draw=red,thick,->,line width=2pt] (1) edge [bend right=15] (4);
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\path[draw=red,thick,->,line width=2pt] (4) edge [bend right=15] (7);
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\path[draw=red,thick,->,line width=2pt] (7) edge [bend right=15] (4);
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\path[draw=red,thick,->,line width=2pt] (4) edge [bend right=15] (8);
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\path[draw=red,thick,->,line width=2pt] (8) edge [bend right=15] (4);
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\path[draw=red,thick,->,line width=2pt] (4) edge [bend right=15] (9);
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\path[draw=red,thick,->,line width=2pt] (9) edge [bend right=15] (4);
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\path[draw=red,thick,->,line width=2pt] (4) edge [bend right=15] (1);
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\path[draw=red,thick,->,line width=2pt] (1) edge [bend right=15] (5);
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\path[draw=red,thick,->,line width=2pt] (5) edge [bend right=15] (1);
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\end{tikzpicture}
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\end{center}
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Hence, the corresponding tree traversal array is as follows:
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\begin{center}
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\begin{tikzpicture}[scale=0.7]
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\draw (0,0) grid (9,1);
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\node at (0.5,0.5) {$1$};
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\node at (1.5,0.5) {$2$};
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\node at (2.5,0.5) {$6$};
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\node at (3.5,0.5) {$3$};
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\node at (4.5,0.5) {$4$};
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\node at (5.5,0.5) {$7$};
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\node at (6.5,0.5) {$8$};
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\node at (7.5,0.5) {$9$};
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\node at (8.5,0.5) {$5$};
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%
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% \footnotesize
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% \node at (0.5,1.4) {$1$};
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% \node at (1.5,1.4) {$2$};
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% \node at (2.5,1.4) {$3$};
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% \node at (3.5,1.4) {$4$};
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% \node at (4.5,1.4) {$5$};
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% \node at (5.5,1.4) {$6$};
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% \node at (6.5,1.4) {$7$};
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% \node at (7.5,1.4) {$8$};
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% \node at (8.5,1.4) {$9$};
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\end{tikzpicture}
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\end{center}
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\subsubsection{Subtree queries}
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Each subtree of a tree corresponds to a subarray
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in the tree traversal array such that
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the first element in the subarray is the root node.
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For example, the following subarray contains the
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nodes in the subtree of node $4$:
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\begin{center}
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\begin{tikzpicture}[scale=0.7]
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\fill[color=lightgray] (4,0) rectangle (8,1);
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\draw (0,0) grid (9,1);
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\node at (0.5,0.5) {$1$};
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\node at (1.5,0.5) {$2$};
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\node at (2.5,0.5) {$6$};
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\node at (3.5,0.5) {$3$};
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\node at (4.5,0.5) {$4$};
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\node at (5.5,0.5) {$7$};
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\node at (6.5,0.5) {$8$};
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\node at (7.5,0.5) {$9$};
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\node at (8.5,0.5) {$5$};
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%
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% \footnotesize
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% \node at (0.5,1.4) {$1$};
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% \node at (1.5,1.4) {$2$};
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% \node at (2.5,1.4) {$3$};
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% \node at (3.5,1.4) {$4$};
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% \node at (4.5,1.4) {$5$};
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% \node at (5.5,1.4) {$6$};
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% \node at (6.5,1.4) {$7$};
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% \node at (7.5,1.4) {$8$};
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% \node at (8.5,1.4) {$9$};
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\end{tikzpicture}
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\end{center}
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Using this fact, we can efficiently process queries
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that are related to subtrees of a tree.
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As an example, consider a problem where each node
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is assigned a value, and our task is to support
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the following queries:
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\begin{itemize}
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\item update the value of a node
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\item calculate the sum of values in the subtree of a node
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\end{itemize}
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Consider the following tree where the blue numbers
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are the values of the nodes.
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For example, the sum of the subtree of node $4$
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is $3+4+3+1=11$.
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\begin{center}
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\begin{tikzpicture}[scale=0.9]
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\node[draw, circle] (1) at (0,3) {$1$};
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\node[draw, circle] (2) at (-3,1) {$2$};
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\node[draw, circle] (3) at (-1,1) {$3$};
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\node[draw, circle] (4) at (1,1) {$4$};
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\node[draw, circle] (5) at (3,1) {$5$};
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\node[draw, circle] (6) at (-3,-1) {$6$};
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\node[draw, circle] (7) at (-0.5,-1) {$7$};
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\node[draw, circle] (8) at (1,-1) {$8$};
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\node[draw, circle] (9) at (2.5,-1) {$9$};
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\path[draw,thick,-] (1) -- (2);
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\path[draw,thick,-] (1) -- (3);
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\path[draw,thick,-] (1) -- (4);
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\path[draw,thick,-] (1) -- (5);
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\path[draw,thick,-] (2) -- (6);
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\path[draw,thick,-] (4) -- (7);
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\path[draw,thick,-] (4) -- (8);
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\path[draw,thick,-] (4) -- (9);
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\node[color=blue] at (0,3+0.65) {2};
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\node[color=blue] at (-3-0.65,1) {3};
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\node[color=blue] at (-1-0.65,1) {5};
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\node[color=blue] at (1+0.65,1) {3};
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\node[color=blue] at (3+0.65,1) {1};
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\node[color=blue] at (-3,-1-0.65) {4};
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\node[color=blue] at (-0.5,-1-0.65) {4};
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\node[color=blue] at (1,-1-0.65) {3};
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\node[color=blue] at (2.5,-1-0.65) {1};
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\end{tikzpicture}
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\end{center}
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The idea is to construct a tree traversal array that contains
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three values for each node: the identifier of the node,
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the size of the subtree, and the value of the node.
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For example, the array for the above tree is as follows:
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\begin{center}
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\begin{tikzpicture}[scale=0.7]
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\draw (0,1) grid (9,-2);
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\node[left] at (-1,0.5) {node id};
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\node[left] at (-1,-0.5) {subtree size};
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\node[left] at (-1,-1.5) {node value};
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\node at (0.5,0.5) {$1$};
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\node at (1.5,0.5) {$2$};
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\node at (2.5,0.5) {$6$};
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\node at (3.5,0.5) {$3$};
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\node at (4.5,0.5) {$4$};
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\node at (5.5,0.5) {$7$};
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\node at (6.5,0.5) {$8$};
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\node at (7.5,0.5) {$9$};
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\node at (8.5,0.5) {$5$};
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\node at (0.5,-0.5) {$9$};
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\node at (1.5,-0.5) {$2$};
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\node at (2.5,-0.5) {$1$};
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\node at (3.5,-0.5) {$1$};
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\node at (4.5,-0.5) {$4$};
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\node at (5.5,-0.5) {$1$};
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\node at (6.5,-0.5) {$1$};
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\node at (7.5,-0.5) {$1$};
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\node at (8.5,-0.5) {$1$};
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\node at (0.5,-1.5) {$2$};
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\node at (1.5,-1.5) {$3$};
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\node at (2.5,-1.5) {$4$};
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\node at (3.5,-1.5) {$5$};
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\node at (4.5,-1.5) {$3$};
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\node at (5.5,-1.5) {$4$};
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\node at (6.5,-1.5) {$3$};
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\node at (7.5,-1.5) {$1$};
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\node at (8.5,-1.5) {$1$};
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%
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% \footnotesize
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% \node at (0.5,1.4) {$1$};
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% \node at (1.5,1.4) {$2$};
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% \node at (2.5,1.4) {$3$};
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% \node at (3.5,1.4) {$4$};
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% \node at (4.5,1.4) {$5$};
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% \node at (5.5,1.4) {$6$};
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% \node at (6.5,1.4) {$7$};
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% \node at (7.5,1.4) {$8$};
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% \node at (8.5,1.4) {$9$};
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\end{tikzpicture}
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\end{center}
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Using this array, we can calculate the sum of values
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in any subtree by first finding out the size of the subtree
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and then the values of the corresponding nodes.
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For example, the values in the subtree of node $4$
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can be found as follows:
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\begin{center}
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\begin{tikzpicture}[scale=0.7]
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\fill[color=lightgray] (4,1) rectangle (5,0);
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\fill[color=lightgray] (4,0) rectangle (5,-1);
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\fill[color=lightgray] (4,-1) rectangle (8,-2);
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\draw (0,1) grid (9,-2);
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\node[left] at (-1,0.5) {node id};
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\node[left] at (-1,-0.5) {subtree size};
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\node[left] at (-1,-1.5) {node value};
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\node at (0.5,0.5) {$1$};
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\node at (1.5,0.5) {$2$};
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\node at (2.5,0.5) {$6$};
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\node at (3.5,0.5) {$3$};
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\node at (4.5,0.5) {$4$};
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\node at (5.5,0.5) {$7$};
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\node at (6.5,0.5) {$8$};
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\node at (7.5,0.5) {$9$};
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\node at (8.5,0.5) {$5$};
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\node at (0.5,-0.5) {$9$};
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\node at (1.5,-0.5) {$2$};
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\node at (2.5,-0.5) {$1$};
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\node at (3.5,-0.5) {$1$};
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\node at (4.5,-0.5) {$4$};
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\node at (5.5,-0.5) {$1$};
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\node at (6.5,-0.5) {$1$};
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\node at (7.5,-0.5) {$1$};
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\node at (8.5,-0.5) {$1$};
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\node at (0.5,-1.5) {$2$};
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\node at (1.5,-1.5) {$3$};
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\node at (2.5,-1.5) {$4$};
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\node at (3.5,-1.5) {$5$};
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\node at (4.5,-1.5) {$3$};
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\node at (5.5,-1.5) {$4$};
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\node at (6.5,-1.5) {$3$};
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\node at (7.5,-1.5) {$1$};
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\node at (8.5,-1.5) {$1$};
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%
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% \footnotesize
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% \node at (0.5,1.4) {$1$};
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% \node at (1.5,1.4) {$2$};
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% \node at (2.5,1.4) {$3$};
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% \node at (3.5,1.4) {$4$};
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% \node at (4.5,1.4) {$5$};
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% \node at (5.5,1.4) {$6$};
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% \node at (6.5,1.4) {$7$};
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% \node at (7.5,1.4) {$8$};
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% \node at (8.5,1.4) {$9$};
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\end{tikzpicture}
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\end{center}
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To answer the queries efficiently,
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it suffices to store the values of the
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nodes in a binary indexed tree or segment tree.
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After this, we can both update a value
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and calculate the sum of values in $O(\log n)$ time.
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\subsubsection{Path queries}
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|
|
Using a tree traversal array, we can also efficiently
|
|
calculate sums of values on
|
|
paths from the root node to any other
|
|
node in the tree.
|
|
Let us next consider a problem where our task
|
|
is to support the following queries:
|
|
\begin{itemize}
|
|
\item change the value of a node
|
|
\item calculate the sum of values on a path from
|
|
the root to a node
|
|
\end{itemize}
|
|
|
|
For example, in the following tree,
|
|
the sum of values from the root node to node 7 is
|
|
$4+5+5=14$:
|
|
|
|
\begin{center}
|
|
\begin{tikzpicture}[scale=0.9]
|
|
\node[draw, circle] (1) at (0,3) {$1$};
|
|
\node[draw, circle] (2) at (-3,1) {$2$};
|
|
\node[draw, circle] (3) at (-1,1) {$3$};
|
|
\node[draw, circle] (4) at (1,1) {$4$};
|
|
\node[draw, circle] (5) at (3,1) {$5$};
|
|
\node[draw, circle] (6) at (-3,-1) {$6$};
|
|
\node[draw, circle] (7) at (-0.5,-1) {$7$};
|
|
\node[draw, circle] (8) at (1,-1) {$8$};
|
|
\node[draw, circle] (9) at (2.5,-1) {$9$};
|
|
|
|
\path[draw,thick,-] (1) -- (2);
|
|
\path[draw,thick,-] (1) -- (3);
|
|
\path[draw,thick,-] (1) -- (4);
|
|
\path[draw,thick,-] (1) -- (5);
|
|
\path[draw,thick,-] (2) -- (6);
|
|
\path[draw,thick,-] (4) -- (7);
|
|
\path[draw,thick,-] (4) -- (8);
|
|
\path[draw,thick,-] (4) -- (9);
|
|
|
|
\node[color=blue] at (0,3+0.65) {4};
|
|
\node[color=blue] at (-3-0.65,1) {5};
|
|
\node[color=blue] at (-1-0.65,1) {3};
|
|
\node[color=blue] at (1+0.65,1) {5};
|
|
\node[color=blue] at (3+0.65,1) {2};
|
|
\node[color=blue] at (-3,-1-0.65) {3};
|
|
\node[color=blue] at (-0.5,-1-0.65) {5};
|
|
\node[color=blue] at (1,-1-0.65) {3};
|
|
\node[color=blue] at (2.5,-1-0.65) {1};
|
|
\end{tikzpicture}
|
|
\end{center}
|
|
|
|
We can solve this problem in a similar way as before,
|
|
but now each value in the last row of the array is the sum of values
|
|
on a path from the root to the node.
|
|
For example, the following array corresponds to the above tree:
|
|
\begin{center}
|
|
\begin{tikzpicture}[scale=0.7]
|
|
\draw (0,1) grid (9,-2);
|
|
|
|
\node[left] at (-1,0.5) {node id};
|
|
\node[left] at (-1,-0.5) {subtree size};
|
|
\node[left] at (-1,-1.5) {path sum};
|
|
|
|
\node at (0.5,0.5) {$1$};
|
|
\node at (1.5,0.5) {$2$};
|
|
\node at (2.5,0.5) {$6$};
|
|
\node at (3.5,0.5) {$3$};
|
|
\node at (4.5,0.5) {$4$};
|
|
\node at (5.5,0.5) {$7$};
|
|
\node at (6.5,0.5) {$8$};
|
|
\node at (7.5,0.5) {$9$};
|
|
\node at (8.5,0.5) {$5$};
|
|
|
|
\node at (0.5,-0.5) {$9$};
|
|
\node at (1.5,-0.5) {$2$};
|
|
\node at (2.5,-0.5) {$1$};
|
|
\node at (3.5,-0.5) {$1$};
|
|
\node at (4.5,-0.5) {$4$};
|
|
\node at (5.5,-0.5) {$1$};
|
|
\node at (6.5,-0.5) {$1$};
|
|
\node at (7.5,-0.5) {$1$};
|
|
\node at (8.5,-0.5) {$1$};
|
|
|
|
\node at (0.5,-1.5) {$4$};
|
|
\node at (1.5,-1.5) {$9$};
|
|
\node at (2.5,-1.5) {$12$};
|
|
\node at (3.5,-1.5) {$7$};
|
|
\node at (4.5,-1.5) {$9$};
|
|
\node at (5.5,-1.5) {$14$};
|
|
\node at (6.5,-1.5) {$12$};
|
|
\node at (7.5,-1.5) {$10$};
|
|
\node at (8.5,-1.5) {$6$};
|
|
%
|
|
% \footnotesize
|
|
% \node at (0.5,1.4) {$1$};
|
|
% \node at (1.5,1.4) {$2$};
|
|
% \node at (2.5,1.4) {$3$};
|
|
% \node at (3.5,1.4) {$4$};
|
|
% \node at (4.5,1.4) {$5$};
|
|
% \node at (5.5,1.4) {$6$};
|
|
% \node at (6.5,1.4) {$7$};
|
|
% \node at (7.5,1.4) {$8$};
|
|
% \node at (8.5,1.4) {$9$};
|
|
\end{tikzpicture}
|
|
\end{center}
|
|
|
|
When the value of a node increases by $x$,
|
|
the sums of all nodes in its subtree increase by $x$.
|
|
For example, if the value of node 4 increases by 1,
|
|
the array changes as follows:
|
|
|
|
\begin{center}
|
|
\begin{tikzpicture}[scale=0.7]
|
|
\fill[color=lightgray] (4,-1) rectangle (8,-2);
|
|
\draw (0,1) grid (9,-2);
|
|
|
|
\node[left] at (-1,0.5) {node id};
|
|
\node[left] at (-1,-0.5) {subtree size};
|
|
\node[left] at (-1,-1.5) {path sum};
|
|
|
|
\node at (0.5,0.5) {$1$};
|
|
\node at (1.5,0.5) {$2$};
|
|
\node at (2.5,0.5) {$6$};
|
|
\node at (3.5,0.5) {$3$};
|
|
\node at (4.5,0.5) {$4$};
|
|
\node at (5.5,0.5) {$7$};
|
|
\node at (6.5,0.5) {$8$};
|
|
\node at (7.5,0.5) {$9$};
|
|
\node at (8.5,0.5) {$5$};
|
|
|
|
\node at (0.5,-0.5) {$9$};
|
|
\node at (1.5,-0.5) {$2$};
|
|
\node at (2.5,-0.5) {$1$};
|
|
\node at (3.5,-0.5) {$1$};
|
|
\node at (4.5,-0.5) {$4$};
|
|
\node at (5.5,-0.5) {$1$};
|
|
\node at (6.5,-0.5) {$1$};
|
|
\node at (7.5,-0.5) {$1$};
|
|
\node at (8.5,-0.5) {$1$};
|
|
|
|
\node at (0.5,-1.5) {$4$};
|
|
\node at (1.5,-1.5) {$9$};
|
|
\node at (2.5,-1.5) {$12$};
|
|
\node at (3.5,-1.5) {$7$};
|
|
\node at (4.5,-1.5) {$10$};
|
|
\node at (5.5,-1.5) {$15$};
|
|
\node at (6.5,-1.5) {$13$};
|
|
\node at (7.5,-1.5) {$11$};
|
|
\node at (8.5,-1.5) {$6$};
|
|
%
|
|
% \footnotesize
|
|
% \node at (0.5,1.4) {$1$};
|
|
% \node at (1.5,1.4) {$2$};
|
|
% \node at (2.5,1.4) {$3$};
|
|
% \node at (3.5,1.4) {$4$};
|
|
% \node at (4.5,1.4) {$5$};
|
|
% \node at (5.5,1.4) {$6$};
|
|
% \node at (6.5,1.4) {$7$};
|
|
% \node at (7.5,1.4) {$8$};
|
|
% \node at (8.5,1.4) {$9$};
|
|
\end{tikzpicture}
|
|
\end{center}
|
|
|
|
Thus, to support both the operations,
|
|
we should be able to increase all values
|
|
in a range and retrieve a single value.
|
|
This can be done in $O(\log n)$ time
|
|
using a binary indexed tree
|
|
or segment tree (see Chapter 9.4).
|
|
|
|
\section{Lowest common ancestor}
|
|
|
|
\index{lowest common ancestor}
|
|
|
|
The \key{lowest common ancestor}
|
|
of two nodes in the tree is the lowest node
|
|
whose subtree contains both the nodes.
|
|
A typical problem is to efficiently process
|
|
queries that ask to find the lowest
|
|
common ancestor of given two nodes.
|
|
|
|
For example, in the following tree,
|
|
the lowest common ancestor of nodes 5 and 8
|
|
is node 2:
|
|
\begin{center}
|
|
\begin{tikzpicture}[scale=0.9]
|
|
\node[draw, circle] (1) at (0,3) {$1$};
|
|
\node[draw, circle] (2) at (2,1) {$4$};
|
|
\node[draw, circle] (3) at (-2,1) {$2$};
|
|
\node[draw, circle] (4) at (0,1) {$3$};
|
|
\node[draw, circle] (5) at (2,-1) {$7$};
|
|
\node[draw, circle, fill=lightgray] (6) at (-3,-1) {$5$};
|
|
\node[draw, circle] (7) at (-1,-1) {$6$};
|
|
\node[draw, circle, fill=lightgray] (8) at (-1,-3) {$8$};
|
|
\path[draw,thick,-] (1) -- (2);
|
|
\path[draw,thick,-] (1) -- (3);
|
|
\path[draw,thick,-] (1) -- (4);
|
|
\path[draw,thick,-] (2) -- (5);
|
|
\path[draw,thick,-] (3) -- (6);
|
|
\path[draw,thick,-] (3) -- (7);
|
|
\path[draw,thick,-] (7) -- (8);
|
|
|
|
\path[draw=red,thick,->,line width=2pt] (6) edge [bend left] (3);
|
|
\path[draw=red,thick,->,line width=2pt] (8) edge [bend right=40] (3);
|
|
\end{tikzpicture}
|
|
\end{center}
|
|
|
|
Next we will discuss two efficient techniques for
|
|
finding the lowest common ancestor of two nodes.
|
|
|
|
\subsubsection{Method 1}
|
|
|
|
One way to solve the problem is to use the fact
|
|
that we can efficiently find the $k$th
|
|
ancestor of any node in the tree.
|
|
Using this, we can divide the problem of
|
|
finding the lowest common ancestor into two parts.
|
|
|
|
We use two pointers that initially point to the
|
|
two nodes for which we should find the
|
|
lowest common ancestor.
|
|
First, we move one of the pointers upwards
|
|
so that both nodes are at the same level in the tree.
|
|
|
|
In the example case, we move from node 8 to node 6,
|
|
after which both nodes are at the same level:
|
|
|
|
\begin{center}
|
|
\begin{tikzpicture}[scale=0.9]
|
|
\node[draw, circle] (1) at (0,3) {$1$};
|
|
\node[draw, circle] (2) at (2,1) {$4$};
|
|
\node[draw, circle] (3) at (-2,1) {$2$};
|
|
\node[draw, circle] (4) at (0,1) {$3$};
|
|
\node[draw, circle] (5) at (2,-1) {$7$};
|
|
\node[draw, circle,fill=lightgray] (6) at (-3,-1) {$5$};
|
|
\node[draw, circle,fill=lightgray] (7) at (-1,-1) {$6$};
|
|
\node[draw, circle] (8) at (-1,-3) {$8$};
|
|
\path[draw,thick,-] (1) -- (2);
|
|
\path[draw,thick,-] (1) -- (3);
|
|
\path[draw,thick,-] (1) -- (4);
|
|
\path[draw,thick,-] (2) -- (5);
|
|
\path[draw,thick,-] (3) -- (6);
|
|
\path[draw,thick,-] (3) -- (7);
|
|
\path[draw,thick,-] (7) -- (8);
|
|
|
|
\path[draw=red,thick,->,line width=2pt] (8) edge [bend right] (7);
|
|
\end{tikzpicture}
|
|
\end{center}
|
|
|
|
After this, we determine the minimum number of steps
|
|
needed to move both pointers upwards so that
|
|
they will point to the same node.
|
|
This node is the lowest common ancestor of the nodes.
|
|
|
|
In the example case, it suffices to move both pointers
|
|
one step upwards to node 2,
|
|
which is the lowest common ancestor:
|
|
|
|
\begin{center}
|
|
\begin{tikzpicture}[scale=0.9]
|
|
\node[draw, circle] (1) at (0,3) {$1$};
|
|
\node[draw, circle] (2) at (2,1) {$4$};
|
|
\node[draw, circle,fill=lightgray] (3) at (-2,1) {$2$};
|
|
\node[draw, circle] (4) at (0,1) {$3$};
|
|
\node[draw, circle] (5) at (2,-1) {$7$};
|
|
\node[draw, circle] (6) at (-3,-1) {$5$};
|
|
\node[draw, circle] (7) at (-1,-1) {$6$};
|
|
\node[draw, circle] (8) at (-1,-3) {$8$};
|
|
\path[draw,thick,-] (1) -- (2);
|
|
\path[draw,thick,-] (1) -- (3);
|
|
\path[draw,thick,-] (1) -- (4);
|
|
\path[draw,thick,-] (2) -- (5);
|
|
\path[draw,thick,-] (3) -- (6);
|
|
\path[draw,thick,-] (3) -- (7);
|
|
\path[draw,thick,-] (7) -- (8);
|
|
|
|
\path[draw=red,thick,->,line width=2pt] (6) edge [bend left] (3);
|
|
\path[draw=red,thick,->,line width=2pt] (7) edge [bend right] (3);
|
|
\end{tikzpicture}
|
|
\end{center}
|
|
|
|
Since both parts of the algorithm can be performed in
|
|
$O(\log n)$ time using precomputed information,
|
|
we can find the lowest common ancestor of any two
|
|
nodes in $O(\log n)$ time using this technique.
|
|
|
|
\subsubsection{Method 2}
|
|
|
|
Another way to solve the problem is based on
|
|
a tree traversal array\footnote{This lowest common ancestor algorithm is based on \cite{ben00}.
|
|
This technique is sometimes called the \index{Euler tour technique}
|
|
\key{Euler tour technique} \cite{tar84}.}.
|
|
Once again, the idea is to traverse the nodes
|
|
using a depth-first search:
|
|
|
|
\begin{center}
|
|
\begin{tikzpicture}[scale=0.9]
|
|
\node[draw, circle] (1) at (0,3) {$1$};
|
|
\node[draw, circle] (2) at (2,1) {$4$};
|
|
\node[draw, circle] (3) at (-2,1) {$2$};
|
|
\node[draw, circle] (4) at (0,1) {$3$};
|
|
\node[draw, circle] (5) at (2,-1) {$7$};
|
|
\node[draw, circle] (6) at (-3,-1) {$5$};
|
|
\node[draw, circle] (7) at (-1,-1) {$6$};
|
|
\node[draw, circle] (8) at (-1,-3) {$8$};
|
|
\path[draw,thick,-] (1) -- (2);
|
|
\path[draw,thick,-] (1) -- (3);
|
|
\path[draw,thick,-] (1) -- (4);
|
|
\path[draw,thick,-] (2) -- (5);
|
|
\path[draw,thick,-] (3) -- (6);
|
|
\path[draw,thick,-] (3) -- (7);
|
|
\path[draw,thick,-] (7) -- (8);
|
|
|
|
\path[draw=red,thick,->,line width=2pt] (1) edge [bend right=15] (3);
|
|
\path[draw=red,thick,->,line width=2pt] (3) edge [bend right=15] (6);
|
|
\path[draw=red,thick,->,line width=2pt] (6) edge [bend right=15] (3);
|
|
\path[draw=red,thick,->,line width=2pt] (3) edge [bend right=15] (7);
|
|
\path[draw=red,thick,->,line width=2pt] (7) edge [bend right=15] (8);
|
|
\path[draw=red,thick,->,line width=2pt] (8) edge [bend right=15] (7);
|
|
\path[draw=red,thick,->,line width=2pt] (7) edge [bend right=15] (3);
|
|
\path[draw=red,thick,->,line width=2pt] (3) edge [bend right=15] (1);
|
|
\path[draw=red,thick,->,line width=2pt] (1) edge [bend right=15] (4);
|
|
\path[draw=red,thick,->,line width=2pt] (4) edge [bend right=15] (1);
|
|
\path[draw=red,thick,->,line width=2pt] (1) edge [bend right=15] (2);
|
|
\path[draw=red,thick,->,line width=2pt] (2) edge [bend right=15] (5);
|
|
\path[draw=red,thick,->,line width=2pt] (5) edge [bend right=15] (2);
|
|
\path[draw=red,thick,->,line width=2pt] (2) edge [bend right=15] (1);
|
|
\end{tikzpicture}
|
|
\end{center}
|
|
|
|
However, we use a bit different tree
|
|
traversal array than before:
|
|
we add each node to the array \emph{always}
|
|
when the depth-first search walks through the node,
|
|
and not only at the first visit.
|
|
Hence, a node that has $k$ children appears $k+1$ times
|
|
in the array and there are a total of $2n-1$
|
|
nodes in the array.
|
|
|
|
We store two values in the array:
|
|
the identifier of the node and the level of the
|
|
node in the tree.
|
|
The following array corresponds to the above tree:
|
|
|
|
\begin{center}
|
|
\begin{tikzpicture}[scale=0.7]
|
|
|
|
\node[left] at (-1,1.5) {node id};
|
|
\node[left] at (-1,0.5) {level};
|
|
|
|
\draw (0,1) grid (15,2);
|
|
%\node at (-1.1,1.5) {\texttt{node}};
|
|
\node at (0.5,1.5) {$1$};
|
|
\node at (1.5,1.5) {$2$};
|
|
\node at (2.5,1.5) {$5$};
|
|
\node at (3.5,1.5) {$2$};
|
|
\node at (4.5,1.5) {$6$};
|
|
\node at (5.5,1.5) {$8$};
|
|
\node at (6.5,1.5) {$6$};
|
|
\node at (7.5,1.5) {$2$};
|
|
\node at (8.5,1.5) {$1$};
|
|
\node at (9.5,1.5) {$3$};
|
|
\node at (10.5,1.5) {$1$};
|
|
\node at (11.5,1.5) {$4$};
|
|
\node at (12.5,1.5) {$7$};
|
|
\node at (13.5,1.5) {$4$};
|
|
\node at (14.5,1.5) {$1$};
|
|
|
|
|
|
\draw (0,0) grid (15,1);
|
|
%\node at (-1.1,0.5) {\texttt{depth}};
|
|
\node at (0.5,0.5) {$1$};
|
|
\node at (1.5,0.5) {$2$};
|
|
\node at (2.5,0.5) {$3$};
|
|
\node at (3.5,0.5) {$2$};
|
|
\node at (4.5,0.5) {$3$};
|
|
\node at (5.5,0.5) {$4$};
|
|
\node at (6.5,0.5) {$3$};
|
|
\node at (7.5,0.5) {$2$};
|
|
\node at (8.5,0.5) {$1$};
|
|
\node at (9.5,0.5) {$2$};
|
|
\node at (10.5,0.5) {$1$};
|
|
\node at (11.5,0.5) {$2$};
|
|
\node at (12.5,0.5) {$3$};
|
|
\node at (13.5,0.5) {$2$};
|
|
\node at (14.5,0.5) {$1$};
|
|
|
|
\footnotesize
|
|
\node at (0.5,2.5) {$0$};
|
|
\node at (1.5,2.5) {$1$};
|
|
\node at (2.5,2.5) {$2$};
|
|
\node at (3.5,2.5) {$3$};
|
|
\node at (4.5,2.5) {$4$};
|
|
\node at (5.5,2.5) {$5$};
|
|
\node at (6.5,2.5) {$6$};
|
|
\node at (7.5,2.5) {$7$};
|
|
\node at (8.5,2.5) {$8$};
|
|
\node at (9.5,2.5) {$9$};
|
|
\node at (10.5,2.5) {$10$};
|
|
\node at (11.5,2.5) {$11$};
|
|
\node at (12.5,2.5) {$12$};
|
|
\node at (13.5,2.5) {$13$};
|
|
\node at (14.5,2.5) {$14$};
|
|
\end{tikzpicture}
|
|
\end{center}
|
|
|
|
Using this array, we can find the lowest common ancestor
|
|
of nodes $a$ and $b$ by finding the node with lowest level
|
|
between nodes $a$ and $b$ in the array.
|
|
For example, the lowest common ancestor of nodes $5$ and $8$
|
|
can be found as follows:
|
|
|
|
\begin{center}
|
|
\begin{tikzpicture}[scale=0.7]
|
|
|
|
\node[left] at (-1,1.5) {node id};
|
|
\node[left] at (-1,0.5) {level};
|
|
|
|
\fill[color=lightgray] (2,1) rectangle (3,2);
|
|
\fill[color=lightgray] (5,1) rectangle (6,2);
|
|
\fill[color=lightgray] (2,0) rectangle (6,1);
|
|
|
|
\node at (3.5,-0.5) {$\uparrow$};
|
|
|
|
\draw (0,1) grid (15,2);
|
|
\node at (0.5,1.5) {$1$};
|
|
\node at (1.5,1.5) {$2$};
|
|
\node at (2.5,1.5) {$5$};
|
|
\node at (3.5,1.5) {$2$};
|
|
\node at (4.5,1.5) {$6$};
|
|
\node at (5.5,1.5) {$8$};
|
|
\node at (6.5,1.5) {$6$};
|
|
\node at (7.5,1.5) {$2$};
|
|
\node at (8.5,1.5) {$1$};
|
|
\node at (9.5,1.5) {$3$};
|
|
\node at (10.5,1.5) {$1$};
|
|
\node at (11.5,1.5) {$4$};
|
|
\node at (12.5,1.5) {$7$};
|
|
\node at (13.5,1.5) {$4$};
|
|
\node at (14.5,1.5) {$1$};
|
|
|
|
|
|
\draw (0,0) grid (15,1);
|
|
\node at (0.5,0.5) {$1$};
|
|
\node at (1.5,0.5) {$2$};
|
|
\node at (2.5,0.5) {$3$};
|
|
\node at (3.5,0.5) {$2$};
|
|
\node at (4.5,0.5) {$3$};
|
|
\node at (5.5,0.5) {$4$};
|
|
\node at (6.5,0.5) {$3$};
|
|
\node at (7.5,0.5) {$2$};
|
|
\node at (8.5,0.5) {$1$};
|
|
\node at (9.5,0.5) {$2$};
|
|
\node at (10.5,0.5) {$1$};
|
|
\node at (11.5,0.5) {$2$};
|
|
\node at (12.5,0.5) {$3$};
|
|
\node at (13.5,0.5) {$2$};
|
|
\node at (14.5,0.5) {$1$};
|
|
|
|
\footnotesize
|
|
\node at (0.5,2.5) {$0$};
|
|
\node at (1.5,2.5) {$1$};
|
|
\node at (2.5,2.5) {$2$};
|
|
\node at (3.5,2.5) {$3$};
|
|
\node at (4.5,2.5) {$4$};
|
|
\node at (5.5,2.5) {$5$};
|
|
\node at (6.5,2.5) {$6$};
|
|
\node at (7.5,2.5) {$7$};
|
|
\node at (8.5,2.5) {$8$};
|
|
\node at (9.5,2.5) {$9$};
|
|
\node at (10.5,2.5) {$10$};
|
|
\node at (11.5,2.5) {$11$};
|
|
\node at (12.5,2.5) {$12$};
|
|
\node at (13.5,2.5) {$13$};
|
|
\node at (14.5,2.5) {$14$};
|
|
\end{tikzpicture}
|
|
\end{center}
|
|
|
|
Node 5 is at position 2, node 8 is at position 5,
|
|
and the node with lowest level between
|
|
positions $2 \ldots 5$ is node 2 at position 3
|
|
whose level is 2.
|
|
Thus, the lowest common ancestor of
|
|
nodes 5 and 8 is node 2.
|
|
|
|
Thus, to find the lowest common ancestor
|
|
of two nodes it suffices to process a range
|
|
minimum query.
|
|
Since the array is static,
|
|
we can process such queries in $O(1)$ time
|
|
after an $O(n \log n)$ time preprocessing.
|
|
|
|
\subsubsection{Distances of nodes}
|
|
|
|
Finally, let us consider the problem of
|
|
finding the distance between
|
|
two nodes in the tree, which equals
|
|
the length of the path between them.
|
|
It turns out that this problem reduces to
|
|
finding the lowest common ancestor of the nodes.
|
|
|
|
First, we root the tree arbitrarily.
|
|
After this, the distance between nodes $a$ and $b$
|
|
can be calculated using the formula
|
|
\[d(a)+d(b)-2 \cdot d(c),\]
|
|
where $c$ is the lowest common ancestor of $a$ and $b$
|
|
and $d(s)$ denotes the distance from the root node
|
|
to node $s$.
|
|
For example, in the tree
|
|
|
|
\begin{center}
|
|
\begin{tikzpicture}[scale=0.9]
|
|
\node[draw, circle] (1) at (0,3) {$1$};
|
|
\node[draw, circle] (2) at (2,1) {$4$};
|
|
\node[draw, circle] (3) at (-2,1) {$2$};
|
|
\node[draw, circle] (4) at (0,1) {$3$};
|
|
\node[draw, circle] (5) at (2,-1) {$7$};
|
|
\node[draw, circle] (6) at (-3,-1) {$5$};
|
|
\node[draw, circle] (7) at (-1,-1) {$6$};
|
|
\node[draw, circle] (8) at (-1,-3) {$8$};
|
|
\path[draw,thick,-] (1) -- (2);
|
|
\path[draw,thick,-] (1) -- (3);
|
|
\path[draw,thick,-] (1) -- (4);
|
|
\path[draw,thick,-] (2) -- (5);
|
|
\path[draw,thick,-] (3) -- (6);
|
|
\path[draw,thick,-] (3) -- (7);
|
|
\path[draw,thick,-] (7) -- (8);
|
|
|
|
\path[draw=red,thick,-,line width=2pt] (8) -- node[font=\small] {} (7);
|
|
\path[draw=red,thick,-,line width=2pt] (7) -- node[font=\small] {} (3);
|
|
\path[draw=red,thick,-,line width=2pt] (6) -- node[font=\small] {} (3);
|
|
\end{tikzpicture}
|
|
\end{center}
|
|
the lowest common ancestor of nodes 5 and 8 is node 2.
|
|
A path from node 5 to node 8
|
|
first ascends from node 5 to node 2
|
|
and then descends from node 2 to node 8.
|
|
The distances of the nodes from the root are
|
|
$d(5)=3$, $d(8)=4$ and $d(2)=2$,
|
|
so the distance between nodes 5 and 8 is
|
|
$3+4-2\cdot2=3$.
|
|
|
|
\section{Offline algorithms}
|
|
|
|
So far, we have discussed \emph{online} algorithms
|
|
that are able to efficiently process queries
|
|
one after another in a given order.
|
|
In particular, we may require that the algorithm
|
|
processes each query before receiving the next query.
|
|
|
|
However, in many problems, the online
|
|
property is not necessary.
|
|
In this section, we focus on \emph{offline} algorithms
|
|
that are given a set of queries that can be
|
|
processed in any order.
|
|
It is often easier to design an offline algorithm
|
|
compared to an online algorithm.
|
|
|
|
\subsubsection{Merging data structures}
|
|
|
|
One method to construct an offline algorithm
|
|
is to traverse the tree recursively
|
|
and maintain data structures for processing queries.
|
|
At each node $s$, we create a data structure
|
|
$\texttt{d}[s]$ that is based on the
|
|
data structures of the children of $s$.
|
|
Then, using this data structure,
|
|
all queries related to $s$ are processed.
|
|
|
|
As an example, consider the following problem:
|
|
We are given a tree where each node has some value.
|
|
Our task is to process queries of the form
|
|
''calculate the number of nodes with value $x$
|
|
in the subtree of node $s$''.
|
|
For example, in the following tree,
|
|
the subtree of node $4$ contains two nodes
|
|
whose value is 3.
|
|
|
|
\begin{center}
|
|
\begin{tikzpicture}[scale=0.9]
|
|
\node[draw, circle] (1) at (0,3) {$1$};
|
|
\node[draw, circle] (2) at (-3,1) {$2$};
|
|
\node[draw, circle] (3) at (-1,1) {$3$};
|
|
\node[draw, circle] (4) at (1,1) {$4$};
|
|
\node[draw, circle] (5) at (3,1) {$5$};
|
|
\node[draw, circle] (6) at (-3,-1) {$6$};
|
|
\node[draw, circle] (7) at (-0.5,-1) {$7$};
|
|
\node[draw, circle] (8) at (1,-1) {$8$};
|
|
\node[draw, circle] (9) at (2.5,-1) {$9$};
|
|
|
|
\path[draw,thick,-] (1) -- (2);
|
|
\path[draw,thick,-] (1) -- (3);
|
|
\path[draw,thick,-] (1) -- (4);
|
|
\path[draw,thick,-] (1) -- (5);
|
|
\path[draw,thick,-] (2) -- (6);
|
|
\path[draw,thick,-] (4) -- (7);
|
|
\path[draw,thick,-] (4) -- (8);
|
|
\path[draw,thick,-] (4) -- (9);
|
|
|
|
\node[color=blue] at (0,3+0.65) {2};
|
|
\node[color=blue] at (-3-0.65,1) {3};
|
|
\node[color=blue] at (-1-0.65,1) {5};
|
|
\node[color=blue] at (1+0.65,1) {3};
|
|
\node[color=blue] at (3+0.65,1) {1};
|
|
\node[color=blue] at (-3,-1-0.65) {4};
|
|
\node[color=blue] at (-0.5,-1-0.65) {4};
|
|
\node[color=blue] at (1,-1-0.65) {3};
|
|
\node[color=blue] at (2.5,-1-0.65) {1};
|
|
\end{tikzpicture}
|
|
\end{center}
|
|
|
|
In this problem, we can use map structures
|
|
to answer the queries.
|
|
For example, the maps for node 4 and
|
|
its children are as follows:
|
|
|
|
\begin{center}
|
|
\begin{tikzpicture}[scale=0.7]
|
|
|
|
\node[draw, rectangle] (a) at (4,5.5)
|
|
{
|
|
\footnotesize
|
|
\begin{tabular}{rrr}
|
|
4 \\
|
|
\hline
|
|
1 \\
|
|
\end{tabular}};
|
|
|
|
\node[draw, rectangle] (b) at (8,5.5)
|
|
{
|
|
\footnotesize
|
|
\begin{tabular}{rrr}
|
|
3 \\
|
|
\hline
|
|
1 \\
|
|
\end{tabular}};
|
|
|
|
|
|
\node[draw, rectangle] (c) at (12,5.5)
|
|
{
|
|
\footnotesize
|
|
\begin{tabular}{rr}
|
|
1 \\
|
|
\hline
|
|
1 \\
|
|
\end{tabular}};
|
|
|
|
\node[draw, rectangle] (d) at (8,8.5)
|
|
{
|
|
\footnotesize
|
|
\begin{tabular}{rrr}
|
|
1 & 3 & 4 \\
|
|
\hline
|
|
1 & 2 & 1 \\
|
|
\end{tabular}};
|
|
\path[draw,thick,-] (a) -- (d);
|
|
\path[draw,thick,-] (b) -- (d);
|
|
\path[draw,thick,-] (c) -- (d);
|
|
\end{tikzpicture}
|
|
\end{center}
|
|
|
|
If we create such a data structure for each node,
|
|
we can easily process all given queries,
|
|
because we can handle all queries related
|
|
to a node immediately after creating its
|
|
data structure. For example, the above
|
|
map structure for node 4
|
|
tells us that its subtree
|
|
contains two nodes whose value is 3.
|
|
|
|
However, it would be too slow to create
|
|
all data structures from scratch.
|
|
Instead, at each node $s$,
|
|
we create an initial data structure $\texttt{d}[s]$
|
|
that only contains the value of $s$.
|
|
After this, we go through the children of $s$ and
|
|
\emph{merge} $\texttt{d}[s]$ and
|
|
all data structures
|
|
$\texttt{d}[u]$ where $u$ is a child of $s$.
|
|
|
|
For example, in the above tree, the map
|
|
for node $4$ is created by merging the following maps:
|
|
|
|
\begin{center}
|
|
\begin{tikzpicture}[scale=0.7]
|
|
|
|
\node[draw, rectangle] (a) at (4,5.5)
|
|
{
|
|
\footnotesize
|
|
\begin{tabular}{rrr}
|
|
4 \\
|
|
\hline
|
|
1 \\
|
|
\end{tabular}};
|
|
|
|
\node[draw, rectangle] (b) at (7,5.5)
|
|
{
|
|
\footnotesize
|
|
\begin{tabular}{rrr}
|
|
3 \\
|
|
\hline
|
|
1 \\
|
|
\end{tabular}};
|
|
|
|
\node[draw, rectangle] (c) at (10,5.5)
|
|
{
|
|
\footnotesize
|
|
\begin{tabular}{rr}
|
|
1 \\
|
|
\hline
|
|
1 \\
|
|
\end{tabular}};
|
|
|
|
\node[draw, rectangle] (d) at (1,5.5)
|
|
{
|
|
\footnotesize
|
|
\begin{tabular}{rr}
|
|
3 \\
|
|
\hline
|
|
1 \\
|
|
\end{tabular}};
|
|
|
|
\end{tikzpicture}
|
|
\end{center}
|
|
|
|
Here the first map is the initial data structure
|
|
for node 4,
|
|
and the other three maps correspond to nodes 7, 8 and 9.
|
|
|
|
The merging at node $s$ can be done as follows:
|
|
We go through the children of $s$
|
|
and at each child $u$ merge $\texttt{d}[s]$
|
|
and $\texttt{d}[u]$.
|
|
We always copy the contents from $\texttt{d}[u]$
|
|
to $\texttt{d}[s]$.
|
|
However, before this, we \emph{swap}
|
|
the contents of $\texttt{d}[s]$ and $\texttt{d}[u]$
|
|
if $\texttt{d}[s]$ is smaller than $\texttt{d}[u]$.
|
|
By doing this, each value is copied only $O(\log n)$
|
|
times during the tree traversal,
|
|
which ensures that the algorithm is efficient.
|
|
|
|
To swap the contents of two data structures $a$ and $b$
|
|
efficiently, we can just use the following code:
|
|
\begin{lstlisting}
|
|
swap(a,b);
|
|
\end{lstlisting}
|
|
It is guaranteed that the above code works in constant time
|
|
when $a$ and $b$ are C++ standard library data structures.
|
|
|
|
\subsubsection{Lowest common ancestors}
|
|
|
|
There is also an offline algorithm
|
|
for processing a set of
|
|
lowest common ancestor queries\footnote{This
|
|
algorithm was published by R. E. Tarjan in 1979 \cite{tar79}.}.
|
|
The algorithm is based on the union-find data structure
|
|
(see Chapter 15.2), and the benefit of the algorithm is
|
|
that it is easier to implement than the
|
|
algorithms discussed earlier in this chapter.
|
|
|
|
The algorithm is given as input a set of pairs of nodes,
|
|
and it determines for each such pair the
|
|
lowest common ancestor.
|
|
The algorithm performs a depth-first tree traversal
|
|
and maintains disjoint sets of nodes.
|
|
Initially, each node belongs to a separate set.
|
|
For each set, we also maintain the highest node in the
|
|
tree that belongs to the set.
|
|
|
|
When the algorithm visits a node $x$,
|
|
it goes through all nodes $y$ such that
|
|
the lowest common ancestor of $x$ and $y$
|
|
has to be found.
|
|
If $y$ has already been visited,
|
|
the algorithm reports that the
|
|
lowest common ancestor of $x$ and $y$
|
|
is the highest node in the set of $y$.
|
|
Then, after processing the subtree of $x$,
|
|
the algorithm combines the sets of $x$ and its parent.
|
|
|
|
For example, assume that we would like the lowest
|
|
common ancestor of node pairs $(5,8)$
|
|
and $(2,7)$ in the following tree:
|
|
\begin{center}
|
|
\begin{tikzpicture}[scale=0.85]
|
|
\node[draw, circle] (1) at (0,3) {$1$};
|
|
\node[draw, circle] (2) at (2,1) {$4$};
|
|
\node[draw, circle] (3) at (-2,1) {$2$};
|
|
\node[draw, circle] (4) at (0,1) {$3$};
|
|
\node[draw, circle] (5) at (2,-1) {$7$};
|
|
\node[draw, circle] (6) at (-3,-1) {$5$};
|
|
\node[draw, circle] (7) at (-1,-1) {$6$};
|
|
\node[draw, circle] (8) at (-1,-3) {$8$};
|
|
\path[draw,thick,-] (1) -- (2);
|
|
\path[draw,thick,-] (1) -- (3);
|
|
\path[draw,thick,-] (1) -- (4);
|
|
\path[draw,thick,-] (2) -- (5);
|
|
\path[draw,thick,-] (3) -- (6);
|
|
\path[draw,thick,-] (3) -- (7);
|
|
\path[draw,thick,-] (7) -- (8);
|
|
\end{tikzpicture}
|
|
\end{center}
|
|
|
|
In the following pictures, gray nodes denote visited nodes
|
|
and dashed groups of nodes belong to the same set.
|
|
When the algorithm visits node 8, it notices that
|
|
node 5 has been visited and the highest node
|
|
in its set is 2. Thus, the lowest common ancestor
|
|
of nodes 5 and 8 is 2:
|
|
\begin{center}
|
|
\begin{tikzpicture}[scale=0.85]
|
|
\node[draw, circle, fill=lightgray] (1) at (0,3) {$1$};
|
|
\node[draw, circle] (2) at (2,1) {$4$};
|
|
\node[draw, circle, fill=lightgray] (3) at (-2,1) {$2$};
|
|
\node[draw, circle] (4) at (0,1) {$3$};
|
|
\node[draw, circle] (5) at (2,-1) {$7$};
|
|
\node[draw, circle, fill=lightgray] (6) at (-3,-1) {$5$};
|
|
\node[draw, circle, fill=lightgray] (7) at (-1,-1) {$6$};
|
|
\node[draw, circle, fill=gray] (8) at (-1,-3) {$8$};
|
|
\path[draw,thick,-] (1) -- (2);
|
|
\path[draw,thick,-] (1) -- (3);
|
|
\path[draw,thick,-] (1) -- (4);
|
|
\path[draw,thick,-] (2) -- (5);
|
|
\path[draw,thick,-] (3) -- (6);
|
|
\path[draw,thick,-] (3) -- (7);
|
|
\path[draw,thick,-] (7) -- (8);
|
|
|
|
\draw [red,thick,dashed,line width=2pt,rotate around={-28:(-2,0)}] (-2.9,1.5) rectangle (-1.9,-2);
|
|
|
|
|
|
\draw [red,thick,dashed,line width=2pt] (-1.5,-0.5) rectangle (-0.5,-1.5);
|
|
\draw [red,thick,dashed,line width=2pt] (-1.5,-2.5) rectangle (-0.5,-3.5);
|
|
|
|
\draw [red,thick,dashed,line width=2pt] (0.5,3.5) rectangle (-0.5,2.5);
|
|
\draw [red,thick,dashed,line width=2pt] (0.5,1.5) rectangle (-0.5,0.5);
|
|
\draw [red,thick,dashed,line width=2pt] (2.5,1.5) rectangle (1.5,0.5);
|
|
\draw [red,thick,dashed,line width=2pt] (2.5,-0.5) rectangle (1.5,-1.5);
|
|
\end{tikzpicture}
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\end{center}
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Later, when visiting node 7,
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the algorithm determines that
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the lowest common ancestor of nodes 2 and 7 is 1:
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\begin{center}
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\begin{tikzpicture}[scale=0.85]
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\node[draw, circle, fill=lightgray] (1) at (0,3) {$1$};
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\node[draw, circle, fill=lightgray] (2) at (2,1) {$4$};
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\node[draw, circle, fill=lightgray] (3) at (-2,1) {$2$};
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\node[draw, circle, fill=lightgray] (4) at (0,1) {$3$};
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\node[draw, circle, fill=gray] (5) at (2,-1) {$7$};
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\node[draw, circle, fill=lightgray] (6) at (-3,-1) {$5$};
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\node[draw, circle, fill=lightgray] (7) at (-1,-1) {$6$};
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\node[draw, circle, fill=lightgray] (8) at (-1,-3) {$8$};
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\path[draw,thick,-] (1) -- (2);
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\path[draw,thick,-] (1) -- (3);
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\path[draw,thick,-] (1) -- (4);
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\path[draw,thick,-] (2) -- (5);
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\path[draw,thick,-] (3) -- (6);
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|
\path[draw,thick,-] (3) -- (7);
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\path[draw,thick,-] (7) -- (8);
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|
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\draw [red,thick,dashed,line width=2pt] (0.5,3.5) rectangle (-3.5,-3.5);
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\draw [red,thick,dashed,line width=2pt] (2.5,1.5) rectangle (1.5,0.5);
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\draw [red,thick,dashed,line width=2pt] (2.5,-0.5) rectangle (1.5,-1.5);
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|
|
|
\end{tikzpicture}
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|
\end{center}
|