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Kinetic Priority Queue
A Kinetic Priority Queue is an abstract kinetic data structure. It is a variant of a priority queue designed to maintain the maximum (or minimum) priority element (key-value pair) when the priority of every element is changing as a continuous function of time. Kinetic priority queues have been used as components of several kinetic data structures, as well as to solve some important non-kinetic problems such as the k-set problem and the connected red blue segments intersection problem. Implementations The operations supported are: * : create an empty kinetic priority queue * (or find-min): - return the (or for a ) value stored in the queue at the current virtual time . * : - insert a key into the kinetic queue at the current virtual time, whose value changes as a continuous function of time . * - delete a key at the current virtual time . There are several variants of kinetic priority queues, which support the same basic operations but have different performance guarante ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Abstract Data Type
In computer science, an abstract data type (ADT) is a mathematical model for data types. An abstract data type is defined by its behavior (Semantics (computer science), semantics) from the point of view of a ''User (computing), user'', of the data, specifically in terms of possible values, possible operations on data of this type, and the behavior of these operations. This mathematical model contrasts with data structures, which are concrete representations of data, and are the point of view of an implementer, not a user. Formally, an ADT may be defined as a "class of objects whose logical behavior is defined by a set of values and a set of operations"; this is analogous to an algebraic structure in mathematics. What is meant by "behaviour" varies by author, with the two main types of formal specifications for behavior being ''axiomatic (algebraic) specification'' and an ''abstract model;'' these correspond to axiomatic semantics and operational semantics of an abstract machine, r ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Kinetic Data Structure
A kinetic data structure is a data structure used to track an attribute of a geometric system that is moving continuously. For example, a kinetic convex hull data structure maintains the convex hull of a group of n moving points. The development of kinetic data structures was motivated by computational geometry problems involving physical objects in continuous motion, such as collision or visibility detection in robotics, animation or computer graphics. Overview Kinetic data structures are used on systems where there is a set of values that are changing as a function of time, in a known fashion. So the system has some values, and for each value v, it is known that v=f(t). Kinetic data structures allow queries on a system at the current virtual time t, and two additional operations: *\textrm(t): Advances the system to time t. *\textrm(v,f(t)): Alters the trajectory of value v to f(t), as of the current time. Additional operations may be supported. For example, kinetic data stru ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Priority Queue
In computer science, a priority queue is an abstract data-type similar to a regular queue or stack data structure in which each element additionally has a ''priority'' associated with it. In a priority queue, an element with high priority is served before an element with low priority. In some implementations, if two elements have the same priority, they are served according to the order in which they were enqueued; in other implementations ordering of elements with the same priority remains undefined. While coders often implement priority queues with heaps, they are conceptually distinct from heaps. A priority queue is a concept like a list or a map; just as a list can be implemented with a linked list or with an array, a priority queue can be implemented with a heap or with a variety of other methods such as an unordered array. Operations A priority queue must at least support the following operations: * ''is_empty'': check whether the queue has no elements. * ''insert_wi ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Kinetic Heap
A Kinetic Heap is a kinetic data structure, obtained by the kinetization of a heap. It is designed to store elements (keys associated with priorities) where the priority is changing as a continuous function of time. As a type of kinetic priority queue, it maintains the maximum priority element stored in it. The kinetic heap data structure works by storing the elements as a tree that satisfies the following heap property if is a child node of , then the priority of the element in must be higher than the priority of the element in . This heap property is enforced using certificates along every edge so, like other kinetic data structures, a kinetic heap also contains a priority queue (the event queue) to maintain certificate failure times. Implementation and operations A regular heap can be kinetized by augmenting with a certificate [] for every pair of nodes, such that is a child node of . If the value stored at a node is a function of time, then this certificate is only ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Kinetic Heater
A Kinetic Heater is a kinetic priority queue similar to a kinetic heap, that makes use of randomization to simplify its analysis in a way similar to a treap. Specifically, each element has a random key associated with it in addition to its priority (which changes as a continuous function of time as in all kinetic data structures). The kinetic heater is then simultaneously a binary search tree on the element keys, and a heap on the element priorities. The kinetic heater achieves (expected) asymptotic performance bounds equal to the best kinetic priority queues. In practice however, it is less efficient since the extra random keys need to be stored, and the procedure to handle certificate failure is a (relatively complicated) rotation instead of a simple swap. Implementation If every element has a key and a priority associated with it, then there is a unique tree structure that is simultaneously a search tree on the keys and a heap on the priorities - this structure corresponds to ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Kinetic Hanger
A Kinetic hanger is a randomized version of a kinetic heap whose performance is easy to analyze tightly. A kinetic hanger satisfies the heap property (the priority of each element is higher than the priority of its children) but relaxes the requirement that the tree structure must be strictly balanced, thus insertions and deletions can be randomized. As a result, the structure of the kinetic hanger has the property that it is drawn uniformly at random from the space of all possible heap-like structures on its elements. Implementation The kinetic hanger structure (including certificates and event queue) is exactly the same as the kinetic heap structure, but without the balancing requirement. Thus, it consists of an efficient priority queue (the event queue) to maintain the certificate failure times, as well as a main (not necessarily balanced) heap-like tree structure in which the elements are stored. There is a certificate associated with each edge that enforces the heap propert ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Dynamic Convex Hull
The dynamic convex hull problem is a class of dynamic problems in computational geometry. The problem consists in the maintenance, i.e., keeping track, of the convex hull for input data undergoing a sequence of discrete changes, i.e., when input data elements may be inserted, deleted, or modified. It should be distinguished from the kinetic convex hull, which studies similar problems for continuously moving points. Dynamic convex hull problems may be distinguished by the types of the input data and the allowed types of modification of the input data. Planar point set It is easy to construct an example for which the convex hull contains all input points, but after the insertion of a single point the convex hull becomes a triangle. And conversely, the deletion of a single point may produce the opposite drastic change of the size of the output. Therefore, if the convex hull is required to be reported in traditional way as a polygon, the lower bound for the worst-case computational c ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Kinetic Tournament
A Kinetic Tournament is a kinetic data structure that functions as a priority queue for elements whose priorities change as a continuous function of time. It is implemented analogously to a "tournament" between elements to determine the "winner" (maximum or minimum element), with the Kinetic data structure#Certificates Approach, certificates enforcing the winner of each "match" in the tournament. It supports the usual priority queue operations - ''insert'', ''delete'' and ''find-max''. They are often used as components of other kinetic data structures, such as kinetic closest pair. Implementation A kinetic tournament is organized in a binary tree-like structure, where the leaves contain the elements, and each internal node contains the larger (or smaller) of the elements in its child nodes. Thus, the Tree (data structure), root of the tree contains the maximum (or minimum) element at a given time. The validity of the structure is enforced by creating a certificate at each node ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Ackermann Function
In computability theory, the Ackermann function, named after Wilhelm Ackermann, is one of the simplest and earliest-discovered examples of a total computable function that is not primitive recursive. All primitive recursive functions are total and computable, but the Ackermann function illustrates that not all total computable functions are primitive recursive. After Ackermann's publication of his function (which had three non-negative integer arguments), many authors modified it to suit various purposes, so that today "the Ackermann function" may refer to any of numerous variants of the original function. One common version, the two-argument Ackermann–Péter function is defined as follows for nonnegative integers ''m'' and ''n'': : \begin \operatorname(0, n) & = & n + 1 \\ \operatorname(m+1, 0) & = & \operatorname(m, 1) \\ \operatorname(m+1, n+1) & = & \operatorname(m, \operatorname(m+1, n)) \end Its value grows rapidly, even for small inputs. For example, is an integer o ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Upper Envelope
In mathematics, the lower envelope or pointwise minimum of a finite set of functions is the pointwise minimum of the functions, the function whose value at every point is the minimum of the values of the functions in the given set. The concept of a lower envelope can also be extended to partial functions by taking the minimum only among functions that have values at the point. The upper envelope or pointwise maximum is defined symmetrically. For an infinite set of functions, the same notions may be defined using the infimum in place of the minimum, and the supremum in place of the maximum. For continuous functions from a given class, the lower or upper envelope is a piecewise function whose pieces are from the same class. For functions of a single real variable whose graphs have a bounded number of intersection points, the complexity of the lower or upper envelope can be bounded using Davenport–Schinzel sequences, and these envelopes can be computed efficiently by a divide-and-c ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Kinetic Closest Pair
A kinetic closest pair data structure is a kinetic data structure that maintains the closest pair of points, given a set ''P'' of ''n'' points that are moving continuously with time in a metric space. While many Kinetic data structure#Performance, efficient algorithms were known in the static case, they proved hard to Kinetic data structure#Certificates Approach, kinetize, so new static algorithms were developed to solve this problem. 2D case Approach 1 The simplest kinetic approach for maintenance of the closest pair is to use variants of the Delaunay triangulations. Consider a hexagon and partition it into six equilateral triangles, and then create a Delaunay triangulation based on each equilateral triangle, as each one is a convex shape. The union of these six Delaunay triangulations, so called Equilateral Delaunay graph (EDG), is a supergraph for the nearest neighbor graph (NNG); the endpoints of the edge with minimum length in EDG gives the closest pair. It is straightf ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
