Lowest common ancestor explained

In graph theory and computer science, the lowest common ancestor (LCA) (also called least common ancestor) of two nodes and in a tree or directed acyclic graph (DAG) is the lowest (i.e. deepest) node that has both and as descendants, where we define each node to be a descendant of itself (so if has a direct connection from, is the lowest common ancestor).

The LCA of and in is the shared ancestor of and that is located farthest from the root. Computation of lowest common ancestors may be useful, for instance, as part of a procedure for determining the distance between pairs of nodes in a tree: the distance from to can be computed as the distance from the root to, plus the distance from the root to, minus twice the distance from the root to their lowest common ancestor .

In a tree data structure where each node points to its parent, the lowest common ancestor can be easily determined by finding the first intersection of the paths from and to the root. In general, the computational time required for this algorithm is where is the height of the tree (length of longest path from a leaf to the root). However, there exist several algorithms for processing trees so that lowest common ancestors may be found more quickly. Tarjan's off-line lowest common ancestors algorithm, for example, preprocesses a tree in linear time to provide constant-time LCA queries. In general DAGs, similar algorithms exist, but with super-linear complexity.

History

The lowest common ancestor problem was defined by, but were the first to develop an optimally efficient lowest common ancestor data structure. Their algorithm processes any tree in linear time, using a heavy path decomposition, so that subsequent lowest common ancestor queries may be answered in constant time per query. However, their data structure is complex and difficult to implement. Tarjan also found a simpler but less efficient algorithm, based on the union-find data structure, for computing lowest common ancestors of an offline batch of pairs of nodes.

simplified the data structure of Harel and Tarjan, leading to an implementable structure with the same asymptotic preprocessing and query time bounds. Their simplification is based on the principle that, in two special kinds of trees, lowest common ancestors are easy to determine: if the tree is a path, then the lowest common ancestor can be computed simply from the minimum of the levels of the two queried nodes, while if the tree is a complete binary tree, the nodes may be indexed in such a way that lowest common ancestors reduce to simple binary operations on the indices. The structure of Schieber and Vishkin decomposes any tree into a collection of paths, such that the connections between the paths have the structure of a binary tree, and combines both of these two simpler indexing techniques.

discovered a completely new way to answer lowest common ancestor queries, again achieving linear preprocessing time with constant query time. Their method involves forming an Euler tour of a graph formed from the input tree by doubling every edge, and using this tour to write a sequence of level numbers of the nodes in the order the tour visits them; a lowest common ancestor query can then be transformed into a query that seeks the minimum value occurring within some subinterval of this sequence of numbers. They then handle this range minimum query problem (RMQ) by combining two techniques, one technique based on precomputing the answers to large intervals that have sizes that are powers of two, and the other based on table lookup for small-interval queries. This method was later presented in a simplified form by . As had been previously observed by, the range minimum problem can in turn be transformed back into a lowest common ancestor problem using the technique of Cartesian trees.

Further simplifications were made by and .

proposed the dynamic LCA variant of the problem in which the data structure should be prepared to handle LCA queries intermixed with operations that change the tree (that is, rearrange the tree by adding and removing edges). This variant can be solved in

O( log N) time in the total size of the tree for all modifications and queries. This is done by maintaining the forest using the dynamic trees data structure with partitioning by size; this then maintains a heavy-light decomposition of each tree, and allows LCA queries to be carried out in logarithmic time in the size of the tree.

Linear space and constant search time solution to LCA in trees

As mentioned above, LCA can be reduced to RMQ. An efficient solution to the resulting RMQ problem starts by partitioning the number sequence into blocks. Two different techniques are used for queries across blocks and within blocks.

Reduction from LCA to RMQ

Reduction of LCA to RMQ starts by walking the tree. For each node visited, record in sequence its label and depth.Suppose nodes and occur in positions and in this sequence, respectively. Then the LCA of and will be found in position RMQ, where the RMQ is taken over the depth values.

Linear space and constant search time algorithm for RMQ reduced from LCA

Despite that there exists a constant time and linear space solution for general RMQ, but a simplified solution can be applied that make uses of LCA’s properties. This simplified solution can only be used for RMQ reduced from LCA.

Similar to the solution mentioned above, we divide the sequence into each block

B _{i} , where each block B _{i} has size of b = { 1 \over 2 } log n . By splitting the sequence into blocks, the RMQ(i,j)  query can be solved by solving two different cases:

Case 1: if i and j are in different blocks

To answer the

RMQ(i,j) query in case one, there are 3 groups of variables precomputed to help reduce query time.

First, the minimum element with the smallest index in each block

B _{i} is precomputed and denoted as y _{i} . A set of y _{i} takes O(n/b) space.

Second, given the set of

y _{i} , the RMQ query for this set is precomputed using the solution with constant time and linearithmic space. There are n/b blocks, so the lookup table in that solution takes O({n \over b} log {n \over b}) space. Because b = { 1 \over 2 } log n , O({n \over b} log {n \over b}) = O(n) space. Hence, the precomputed RMQ query using the solution with constant time and linearithmic space on these blocks only take O(n) space.

Third, in each block

B _{i} , let k _{i} be an index in B _{i} such that 0 \leq ki < b . For all k _{i} from 0 until b , block B _{i} is divided into two intervals [ 0 , k _{i)} and [k _{i,} b) . Then the minimum element with the smallest index for intervals in [ 0 , k _{i)} and [k _{i,} b) in each block B _{i} is precomputed. Such minimum elements are called as prefix min for the interval in [ 0 , k _{i)} and suffix min for the interval in [k _{i,} b) . Each iteration of k _{i} computes a pair of prefix min and suffix min. Hence, the total number of prefix mins and suffix mins in a block B _{i} is 2 b . Since there are n/b blocks, in total, all prefix min and suffix min arrays take O( 2 b ⋅ {n \over b}) which is O(n) spaces.

In total, it takes

O(n) space to store all 3 groups of precomputed variables mentioned above.

Therefore, answering the

RMQ(i,j) query in case 1 is simply tasking the minimum of the following three questions:

Let

B _{i} be the block that contains the element at index i , and B _{j} for index j .

1. The suffix min in

[i \mod b, b) in the blockB _{i}

1. Answering the RMQ query on a subset of

y s from blocks \{ B _{i+1} ... B _{j-1} \} using the solution with constant time and linearithmic space

1. The prefix min in

[ 0 , j \mod b) in the block B _{j}

All 3 questions can be answered in constant time. Hence, case 1 can be answered in linear space and constant time.

Case 2: if i and j are in the same block

The sequence of RMQ that reduced from LCA has one property that a normal RMQ doesn’t have. The next element is always +1 or -1 from the current element. For example:Therefore, each block

B _{i} can be encoded as a bitstring with 0 represents the current depth -1, and 1 represent the current depth +1. This transformation turns a block B _{i} into a bitstring of size b -1 . A bitstring of size b -1 has 2 ^{b-1} possible bitstrings. Since b = { 1 \over 2 } log n , so 2 ^{b-1} \leq 2 ^{b} = 2 ^{{1} log n} = n ^{{1} } = \sqrt.

Hence,

B _{i} is always one of the \sqrt{n} possible bitstring with size of b -1 .

Then, for each possible bitstrings, we apply the naïve quadratic space constant time solution. This will take up

\sqrt{n} ⋅ b ^{2} spaces, which is O(\sqrt{n} ⋅ ( log n) ^{2)} \le O(\sqrt{n} ⋅ \sqrt{n}) = O(n) .

Therefore, answering the

RMQ(i,j) query in case 2 is simply finding the corresponding block (in which is a bitstring) and perform a table lookup for that bitstring. Hence, case 2 can be solved using linear space with constant searching time.

Extension to directed acyclic graphs

While originally studied in the context of trees, the notion of lowest common ancestors can be defined for directed acyclic graphs (DAGs), using either of two possible definitions. In both, the edges of the DAG are assumed to point from parents to children.

• Given, define a poset such that iff is reachable from . The lowest common ancestors of and are then the minimum elements under ≤ of the common ancestor set .
• gave an equivalent definition, where the lowest common ancestors of and are the nodes of out-degree zero in the subgraph of induced by the set of common ancestors of and .

In a tree, the lowest common ancestor is unique; in a DAG of nodes, each pair of nodes may have as much as LCAs, while the existence of an LCA for a pair of nodes is not even guaranteed in arbitrary connected DAGs.

A brute-force algorithm for finding lowest common ancestors is given by : find all ancestors of and, then return the maximum element of the intersection of the two sets. Better algorithms exist that, analogous to the LCA algorithms on trees, preprocess a graph to enable constant-time LCA queries. The problem of LCA existence can be solved optimally for sparse DAGs by means of an algorithm due to .

present a unified framework for preprocessing directed acyclic graphs to compute a representative lowest common ancestor in a rooted DAG in constant time. Their framework can achieve near-linear preprocessing times for sparse graphs and is available for public use.^[1]

Applications

The problem of computing lowest common ancestors of classes in an inheritance hierarchy arises in the implementation of object-oriented programming systems . The LCA problem also finds applications in models of complex systems found in distributed computing .

See also

• Level ancestor problem
• Semilattice

References

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External links

• Lowest Common Ancestor of a Binary Search Tree, by Kamal Rawat
• Python implementation of the algorithm of Bender and Farach-Colton for trees, by David Eppstein
• Python implementation for arbitrary directed acyclic graphs
• Lecture notes on LCAs from a 2003 MIT Data Structures course. Course by Erik Demaine, notes written by Loizos Michael and Christos Kapoutsis. Notes from 2007 offering of same course, written by Alison Cichowlas.
• Lowest Common Ancestor in Binary Trees in C. A simplified version of the Schieber–Vishkin technique that works only for balanced binary trees.
• Video of Donald Knuth explaining the Schieber–Vishkin technique
• Range Minimum Query and Lowest Common Ancestor article in Topcoder
• Documentation for the lca package for Haskell by Edward Kmett, which includes the skew-binary random access list algorithm. Purely functional data structures for on-line LCA slides for the same package.

Notes and References

1. Web site: Try our source code for free.