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Dynamic Planar Convex Hull Operations in Near
Dynamic Planar Convex Hull Operations in Near

... To insert a segment s to S, we first insert its two endpoints to the tree as described above. Then we find the lowest node v whose slab contains s and insert s to S v . To delete a segment s from S, we identify the node v that stores s and delete s from S v (retaining lazily the endpoints in the tre ...
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Analysis of Algorithms CS 465/665

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Powerpoint Slides

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... ;; We are going to use two helpers. first we find the appropriate node. Then we delete it and replace it by values below. This has to be done recursively (define (find-node N tree) ;;very similar to is-in-tree?. However, here we want to return a node, not a Boolean. (cond ((btree-null? tree) (error ...
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Probabilistic Data Structures for Priority Queues

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Multilevel Indexing and B+ Trees

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PPT - WSU EECS - Washington State University

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... the digits in order to produce integers, the task of implementation is simplified and the relational operators already allow us to determine whether one key is larger than another. If not, then the processes which are required to build the relationships are built into the code. For example, finding ...
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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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