Time O(log d) Exponential-search(13) Finger Search Searching in a sorted array 1 23578111314151718202425262829313334 time O(log n) Binary-search(13) 23578111314151718202425262829313334.

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Presentation transcript:

time O(log d) Exponential-search(13) Finger Search Searching in a sorted array time O(log n) Binary-search(13) Finger d

Dynamic Finger Search 2 SearchInsert/Delete No fingers Red-black, AVL, 2-4-trees,...O(log n) O(1) fixed fingers Guibas et al. 1977,....O(log d)O(1) Each node a finger Level-linked (2,4)-treesO(log d) O(log n) O(1) am. Randomized Skip listsO(log d) exp.O(1) exp. TreapsO(log d) exp.O(1) exp. Brodal, Lagogiannis, Makris, Tsakalidis, Tsichlas 2003 O(log d)O(1)

Level-Linked (2,4)-trees 3 [S. Huddleston, K. Mehlhorn. A new data structure for representing sorted lists. Acta Informatica, 17:157–184, 1982] Potential Φ = 2 ∙ # degree-4 + # degree-2 UpdatesSplit nodes of degree >4, fusion nodes of degree <2 Search Search up + top-down search finger search(T)

Randomized Skip Lists InsertionIncrease pile to next level with pr. = 1/2 HeightO(log n) expected with high probability PointerHorizontally spans O(1) exp. piles one level below Finger Remember nodes on search path 4 [W. Pugh. Skip lists: A probabilistic alternative to balanced trees. Communications of the ACM, 33(6):668–676, 1990] finger search(D)

Treaps – Randomized Binary Search Trees 5 [R. Seidel and C. R. Aragon. Randomized search trees. Algorithmica, 16(4/5):464–497, 1996]  Each element random priority  Search tree wrt element  Heap order wrt priority  Height O(log n) expected  Insert & deletion rotations O(1) expected time  Search Go up to LCA, and search down – concurrently follow excess path to find next LCA candidate Search path O(log d) expected finger Search(P) excess path

 Merging sorted lists L 1 and L 2 / finger search trees  Merging leaf lists in an arbitrary binary tree O(n∙log n) Proof Induction O(log n!) O(log n 1 ! + log n 2 ! + n 1 ∙log ((n 1 +n 2 )/n 1 )) = O(log n 1 ! + log n 2 ! + n 1 ∙log ( ) = O(log n 1 ! + log n 2 ! + (n 1 +n 2 )∙log (n 1 +n 2 ) - log n 1 ! - log n 2 !) = O(log (n 1 +n 2 )!) repeated insertion Application: Binary Merging 6 [S. Huddleston, K. Mehlhorn. A new data structure for representing sorted lists. Acta Informatica, 17:157–184, 1982] L1L1 L2L2 didi n1+n2n1n1+n2n n2n2 n1n

Maximal Pairs with Bounded Gap ABCDABDBADAADDABDBACABA 7 [G.S. Brodal, R.B. Lyngsø, C.N.S. Pedersen, J. Stoye. Finding Maximal Pairs with Bounded Gap, Journal of Discrete Algorithms, Special Issue of Matching Patterns, volume 1(1), pages , 2000] P P gap  [low,high] ≠ right maximalleft maximal ≠  Build suffix tree (ST) & make it binary  Create leaf lists at each node  Right-maximal pairs = ST nodes  Find maximal pairs = finger search at ST nodes O(n∙log n+k)