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Computational Geometry: Proximity and Location
Computational Geometry: Proximity and Location

... query point. The second binary search tests whether the query point lies above or below individual lines of the slab, in order to determine which trapezoid contains the query point. Since each slab can be intersected by at most n lines, this second search can be done in O(log n) time as well. A stra ...
CE221_week_3_Chapter3_ListStackQueuePart1
CE221_week_3_Chapter3_ListStackQueuePart1

... • The special case of adding to the front or removing the first item is thus a constant-time operation if there exists a link to the front of the list. The same holds when adding at the end as long as another link is provided to the last node. • Removing the last item is trickier! Why? We need to fi ...
CE221_week_3_Chapter3_ListStackQueuePart1
CE221_week_3_Chapter3_ListStackQueuePart1

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data structures and applicatons
data structures and applicatons

...  Deletion of a node. 17. What is the advantage of doubly linked list? For some applications, especially those where it is necessary to traverse list in both direction so, doubly linked list work much better than singly linked list. Doubly linked list is an advanced form of a singly linked list, in ...
insertFront - WSU EECS - Washington State University
insertFront - WSU EECS - Washington State University

... How do we Select a Data Structure? (1) ...
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A Simple Implementation Technique for Priority Search Queues
A Simple Implementation Technique for Priority Search Queues

... by the functional programming community and that deserves to be known better. Priority search queues are an amazing blend of finite maps (or dictionaries) and priority queues, that is, they support both dictionary operations (for instance, accessing a binding with a given key) and priority queue ope ...
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Efficient IP Table Lookup via Adaptive Stratified Trees with - IIT-CNR

... 2004] and [Lampson et al. 1999]; it has been extended in [Feldmann and Muthukrishnan 2000] and in several recent papers [Thorup 2003; Kaplan et al. 2003]. It has been used in [Buchsbaum et al. 2003] to prove formally the equivalence among several different problems. Important asymptotic worst case b ...
A Locality-Preserving Cache-Oblivious Dynamic Dictionary
A Locality-Preserving Cache-Oblivious Dynamic Dictionary

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... 3. Find (X) - Returns the position of X. 4. Next (i) - Returns the position of its successor element i+1. 5. Previous (i) - Returns the position of its predecessor i-1. 6. Print list - Contents of the list is displayed. 7. Makeempty - Makes the list empty. 2.1 .1 Implementation of List ADT 1. Array ...
External Memory Geometric Data Structures
External Memory Geometric Data Structures

... techniques developed in this model often work well in more complex models. Outline of notes. The rest of this note is organized as follows. In Section 2 we discuss the Btree, the most fundamental (one-dimensional) external data structure. In Sections 3 to 5 we then discuss variants of B-trees, namel ...
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Dr-Margush-06-07_LinkedLists

... • List keeps reference to last Node – Facilitates add to end since we always know where the last node is ...
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Notes for Lecture 3 (ppt file)

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Linked Lists Introduction to Linked Lists Node Organization Empty List

... Insert node in a certain position Create the new node, store the data in it Use pointer p to traverse the list, until it points to: node after insertion point or NULL --as p is advancing, make n point to the node before if p points to first node (p is head, n was not set) make head point to new node ...
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Data Structures (CS 1520) Name:___________________________ / -

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ViST: A Dynamic Index Method for Querying XML Data by Tree
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Five Balltree Construction Algorithms
Five Balltree Construction Algorithms

... K-d Construction Algorithm We will call the simplest algorithm the k-d construction algorithm because it is similar to the method described in [Friedman, et. al., 1977] for the construction of k-d trees. It is an off-line top down algorithm. By this we mean that all of the leaf balls must be availab ...
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Slides for Linked List

...  waste space + always need to track/update “size” insert and delete: have prohibitive overheads when the sequences are sorted (or, if we insist on inserting at a given location…) ...
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Interval tree

In computer science, an interval tree is a tree data structure to hold intervals. Specifically, it allows one to efficiently find all intervals that overlap with any given interval or point. It is often used for windowing queries, for instance, to find all roads on a computerized map inside a rectangular viewport, or to find all visible elements inside a three-dimensional scene. A similar data structure is the segment tree.The trivial solution is to visit each interval and test whether it intersects the given point or interval, which requires O(n) time, where n is the number of intervals in the collection. Since a query may return all intervals, for example if the query is a large interval intersecting all intervals in the collection, this is asymptotically optimal; however, we can do better by considering output-sensitive algorithms, where the runtime is expressed in terms of m, the number of intervals produced by the query. Interval trees have a query time of O(log n + m) and an initial creation time of O(n log n), while limiting memory consumption to O(n). After creation, interval trees may be dynamic, allowing efficient insertion and deletion of an interval in O(log n). If the endpoints of intervals are within a small integer range (e.g., in the range [1,...,O(n)]), faster data structures exist with preprocessing time O(n) and query time O(1+m) for reporting m intervals containing a given query point.
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