1 / 36

LCA

LCA. Ariel Rosenfeld Bar-Ilan Uni. The Lowest Common Ancestor. In a rooted tree T , a node u is an ancestor of a node v if u is on the unique path from the root to v .

matty
Download Presentation

LCA

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. LCA Ariel Rosenfeld Bar-Ilan Uni.

  2. The Lowest Common Ancestor • In a rooted tree T, a node u is an ancestorof a node v if u is on the unique pathfrom theroot to v. • In a rooted tree T, the Lowest CommonAncestor (LCA) of two nodes u and v is the deepest node in T that is the ancestor of both u and v.

  3. For example… 1 • Node 3 is the LCA of nodes 4 and 6. • Node 1 is the LCA of node 2 and 5. 2 3 4 5 6

  4. The LCA Problem • The LCA problem is then, given a rooted tree T for preprocessing, preprocess it in a way so that the LCA of any two given nodes in T can be retrieved in constant time.

  5. Prepossessing vs. querying

  6. Complete Binary Tree • Let B denote a complete binary tree with n nodes. • The key here is to encode the unique path from the root to a node in the node itself. We assign each node a path number, a logn bit number that encodes the unique path from the root to the node.

  7. The Path Number For each node v in B we encode a path number in the following way: • Counting from the left most bit, the i’th bit of the path number for v corresponds to the i’th edge on the path from the root to v. • A 0 for the i’th bit from the left indicates that the i’th edge on the path goes to a left child, and a 1 indicates that it goes to a right child. • Let k denote then number of edges on the path from the root to v, then we mark the k+1 bit (the height bit) of the path number 1, and the rest of the logn-k-1bits 0.

  8. For example… 1 0 • Node i’s path number is • Node j’s path number is node j 0 1 0 node i 0 1 0 1 1 0 1 0 The height bit is marked in blue Padded bits are marked in red.

  9. 1000 Path numbers can easily be assigned in a simple O(n) in-order traversal on B. (good programing exercise) 0100 1100 0010 0110 1010 1110 0001 0011 0101 0111 1001 1011 1101 1111

  10. How do we solve LCA queries in B • Suppose now that u and v are two nodes in B, and that path(u) and path(v) are their appropriate path numbers. • We denote the lowest common ancestor of u and v as lca(u,v). • We denote the prefix bits in the path number, those that correspond to edges on the path from the root, as the path bits of the path number.

  11. First we calculate path(u) XOR path(v) and find the left most bit which equals 1. • If there is no such bit than path(u) = path(v) and so u = v, so assume that the k’th bit of the result is 1. • If both the k’th bit in path(u) and the k’th bit in path(v) are path bits, then this means that u and v agree on k-1 edges of their path from the root, meaning that the k-1 prefix of each node’s path number encodes within it the path from the root to lca(u,v).

  12. For example… • path(u) XOR path(v) = lca(u,v) 0100 u 0010 v 0111 0 0 1 0 XOR 0 1 1 1 0 1 0 1 path(lca(u,v) = 0 1 0 0 height bit padded bits

  13. For example… • path(u’) XOR path(v’) = lca(u’,v’) 1010 u’ v’ 1001 1011 1 0 0 1 XOR 1 0 1 1 0 0 1 0 path(lca(u,v) = 1 0 1 0 height bit padded bit

  14. This concludes that if we take the prefix k-1 bits of the result of path(u) XOR path(v), add 1 as the k’th bit, and pad logn-k0 suffix bits, we get path(lca(u,v)). • If either the k’th bit in path(u) or the k’th bit in path(v) (or both) is not a path bit then one node is ancestor to the other, and lca(u,v) can easily be retrieved by comparing path(u) and path(v)’s height bit.

  15. The general LCA algorithm The following are the two stages of the general LCA algorithm for any arbitrary tree T: • First, we reduce the LCA problem to the Restricted Range Minima {RRM} (simple case of Range Minimum Query {RMQ}) problem. • Problem of finding the smallest number in an interval of a fixed list of numbers, where the difference between two successive numbers in the list is exactly one. • We’ll solve the Restricted Range Minima problem and thus solve the LCA problem.

  16. The Reduction • Let T denote an arbitrary tree • Let lca(u,v) denote the lowest common ancestor of nodes u and v in T. • First we execute a depth-first traversal of T to label the nodes in the depth-first order they are encountered. • In that same traversal we maintain a list L, of nodes of T, in the same order that they were visited. • The only property of the depth-first numbering we need is that the number given to any node is smaller then the number given to any of it’s descendents.

  17. For example… 000 The depth-first traversal creates these depth numbers and the following list L: 001 010 011 100 101 110 111 L = { 0, 1, 0, 2, 3, 2, 4, 2, 5, 6, 5, 7, 5, 2, 0 }

  18. Now if want to find lca(u,v), we find the first occurrence of the two nodes in L, this defines an interval I in L. • Suppose u occurs in L before v. Now, I describes the part of the traversal, from the point we first discovered u to the point we first discovered v. • lca(u,v) can be retrieved by finding the minimum number in I.

  19. This is due to the following two simple facts: • If u is an ancestor of v then all nodes visited between u and v are in u’s subtree, thus the depth-number assigned to u is minimal in I. • If u is not an ancestor of v, then all those nodes visited between u and v are in lca(u,v)’s subtree, and the traversal must visit lca(u,v). Thus the minimum of I is the depth-number assigned to lca(u,v).

  20. For example.. 000 • lca(3,7) = 2 001 010 011 100 101 110 111 L = { 0, 1, 0, 2, 3, 2, 4, 2, 5, 6, 5, 7, 5, 2, 0 } • lca(0,7) = 0

  21. The Restricted Reduction • So far we’ve shown how to reduce the LCA problem to the range minima problem. This next step shows how to achieve reduction to the restricted rangeminima problem. • Denote level(u) as the number of edges in the unique path from the root to node u in T. • If L = { l1, l2, … , lz } then we build the following list : L’={level(l1),level(l2),…level(lz)}.

  22. We use L’ in the same manner we used L in the previous reduction scheme. • This works because in every interval I = [u,v] in L, lca(u,v) is the lowest node in I for the same reasons mentioned earlier. • The difference between two adjacent elements in L’ is exactly one. • This completes the reduction to the restricted range minima problem.

  23. The reduction complexity. Denote n as the number of nodes in T. • Depth-first traversal can be done in O( n ) space and time complexity. • L is of size O( n ) and thus it’s creation and initialization can be done in O( n ) space and time complexity. • To find lca(u,v) we need the first occurrence of u and v in L. This could be stored in a table of size O( n ). Thus the creation and initialization of this table can be done in O( n ) space and time complexity. The total space and time complexity of the reduction is then O( n ).

  24. The Range Minima Problem • The Range Minima problem is the problem of finding the smallest number in an interval of a fixed list of numbers. • The Restricted Range Minima problem is an instance of the Range Minima problem where the difference between two successive numbers is exactly one.

  25. More Formally The Restricted Range Minima problem is stated formally in the following: Given a list L = { l1 , l2 , … , ln } of n real numbers, where for each i = 1… n-1 holds that | li - li+1 | = 1 preprocess the list so that for any interval [ li , li+1 , … , lj ] ,1  i < j n, the minimum over the interval can be retrieved in constant time.

  26. How to solve RRM? • I added the article explaining how to solve RRM in O(n) time and space. • Somewhat challenging..

  27. Does it work the other way around? • If LCA had a good solution, would it solve (restricted) Range Maxima? • In other words, Can we reduce RM to LCA? • Surprisingly easy.

  28. Cartesian Tree Construction A[8] A[0] A[1] A[2] A[3] A[4] A[5] A[6] A[7] A[9] 10 25 22 34 7 19 10 12 26 16

  29. Linear Cartesian Tree Construction A[8] A[0] A[1] A[2] A[3] A[4] A[5] A[6] A[7] A[9] 10 25 22 34 7 19 10 12 26 16 10 0

  30. Linear Cartesian Tree Construction A[8] A[0] A[1] A[2] A[3] A[4] A[5] A[6] A[7] A[9] 10 25 22 34 7 19 10 12 26 16 10 0 25 1

  31. Linear Cartesian Tree Construction A[8] A[0] A[1] A[2] A[3] A[4] A[5] A[6] A[7] A[9] 10 25 22 34 7 19 10 12 26 16 10 0 22 2 25 1

  32. Linear Cartesian Tree Construction A[8] A[0] A[1] A[2] A[3] A[4] A[5] A[6] A[7] A[9] 34 7 10 25 22 34 7 19 10 12 26 16 34 10 0 22 2 34 25 1 3 3

  33. Linear Cartesian Tree Construction A[8] A[0] A[1] A[2] A[3] A[4] A[5] A[6] A[7] A[9] 10 25 22 34 7 19 10 12 26 16 7 4 10 0 22 2 34 25 1 3

  34. Linear Cartesian Tree Construction A[8] A[0] A[1] A[2] A[3] A[4] A[5] A[6] A[7] A[9] 10 25 22 34 7 19 10 12 26 16 4 0 6 2 5 7 1 3 9 8

  35. Reduction from RMQ to LCA • If there is an -time solution for LCA, then there is an -time solution for RMQ. • Let A be the input array to the RMQ problem. • Let C be the Cartesian tree of A. • RMQA(i,j) = LCAC(i,j)

  36. Linear Cartesian Tree Construction A[8] A[0] A[1] A[2] A[3] A[4] A[5] A[6] A[7] A[9] 10 25 22 34 7 19 10 12 26 16 4 0 6 2 5 7 1 3 9 8

More Related