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439
Amortized Efficiency of List Update and Paging Rules
, 1985
"... In this article we study the amortized efficiency of the “movetofront” and similar rules for dynamically maintaining a linear list. Under the assumption that accessing the ith element from the front of the list takes 0(i) time, we show that movetofront is within a constant factor of optimum amo ..."
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Cited by 824 (8 self)
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In this article we study the amortized efficiency of the “movetofront” and similar rules for dynamically maintaining a linear list. Under the assumption that accessing the ith element from the front of the list takes 0(i) time, we show that movetofront is within a constant factor of optimum
Learning quickly when irrelevant attributes abound: A new linearthreshold algorithm
 Machine Learning
, 1988
"... learning Boolean functions, linearthreshold algorithms Abstract. Valiant (1984) and others have studied the problem of learning various classes of Boolean functions from examples. Here we discuss incremental learning of these functions. We consider a setting in which the learner responds to each ex ..."
Abstract

Cited by 773 (5 self)
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be expressed as a linearthreshold algorithm. A primary advantage of this algorithm is that the number of mistakes grows only logarithmically with the number of irrelevant attributes in the examples. At the same time, the algorithm is computationally efficient in both time and space. 1.
Dynamic Planar Convex Hull Operations in NearLogarithmic Amortized Time
 JOURNAL OF THE ACM
, 1999
"... We give a data structure that allows arbitrary insertions and deletions on a planar point set P and supports basic queries on the convex hull of P , such as membership and tangentfinding. Updates take O(log 1+" n) amortized time and queries take O(log n) time each, where n is the maximum siz ..."
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Cited by 40 (6 self)
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We give a data structure that allows arbitrary insertions and deletions on a planar point set P and supports basic queries on the convex hull of P , such as membership and tangentfinding. Updates take O(log 1+" n) amortized time and queries take O(log n) time each, where n is the maximum
Amortized Supersampling
"... We present a realtime rendering scheme that reuses shading samples from earlier time frames to achieve practical antialiasing of procedural shaders. Using a reprojection strategy, we maintain several sets of shading estimates at subpixel precision, and incrementally update these such that for most ..."
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Cited by 8 (2 self)
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We present a realtime rendering scheme that reuses shading samples from earlier time frames to achieve practical antialiasing of procedural shaders. Using a reprojection strategy, we maintain several sets of shading estimates at subpixel precision, and incrementally update these such that for most
Polylogarithmic deterministic fullydynamic graph algorithms I: connectivity and minimum spanning tree
 JOURNAL OF THE ACM
, 1997
"... Deterministic fully dynamic graph algorithms are presented for connectivity and minimum spanning forest. For connectivity, starting with no edges, the amortized cost for maintaining a spanning forest is O(log² n) per update, i.e. per edge insertion or deletion. Deciding connectivity between any two ..."
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Cited by 154 (7 self)
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given vertices is done in O(log n= log log n) time. This matches the previous best randomized bounds. The previous best deterministic bound was O( 3 p n log n) amortized time per update but constant time for connectivity queries. For minimum spanning trees, first a deletionsonly algorithm
Amortized Complexity of Data Structures
, 1991
"... This thesis investigates the amortized complexity of some fundamental data structure problems and introduces interesting ideas for proving lower bounds on amortized complexity and for performing amortized analysis. The problems are as follows: ffl Dictionary Problem: A dictionary is a dynamic set t ..."
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Cited by 1 (0 self)
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that supports searches of elements and changes under insertions and deletions of elements. It is open whether there exists a dictionary data structure that takes constant amortized time per operation and uses space polynomial in the dictionary size. We prove that dictionary operations require loglogarithmic
kinetics with logarithmic time update
, 2014
"... In this paper I outline a fast method called KFOLD for implementing the Gillepie algorithm to stochastically sample the folding kinetics of an RNA molecule at single basepair resolution. In the same fashion as the KINFOLD algorithm, which also uses the Gillespie algorithm to predict folding kineti ..."
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/deletion reactions and their corresponding rates do not change between each step in the algorithm. This allows KFOLD to achieve a substantial speedup in the time required to compute a prediction of the folding pathway and, for a fixed number of basepair moves, performs logarithmically with sequence size
Orienting Fully Dynamic Graphs with WorstCase Time Bounds
, 2014
"... In edge orientations, the goal is usually to orient (direct) the edges of an undirected network (modeled by a graph) such that all outdegrees are bounded. When the network is fully dynamic, i.e., admits edge insertions and deletions, we wish to maintain such an orientation while keeping a tab on th ..."
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Cited by 2 (0 self)
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degree and a logarithmic amortized update time for all graphs with constant arboricity, which include all planar and excludedminor graphs. It remained an open question – first proposed by Brodal and Fagerberg, later by Erickson and others – to obtain similar bounds with worstcase update time. We address
Solving online feasibility problem in constant amortized time per update
, 2005
"... We present a deterministic algorithm for solving the two and threedimensional online feasibility problem. Insertion of a new constraint is processed in constant amortized time. Our method is adapted from the offline linear deterministic Megiddo algorithm for linear programming. As in his prune an ..."
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We present a deterministic algorithm for solving the two and threedimensional online feasibility problem. Insertion of a new constraint is processed in constant amortized time. Our method is adapted from the offline linear deterministic Megiddo algorithm for linear programming. As in his prune
LogarithmicTime Updates and Queries in Probabilistic Networks
 Journal of Artificial Intelligence Research
, 1995
"... Traditional databases commonly support efficient query and update procedures that operate in time which is sublinear in the size of the database. Our goal in this paper is to take a first step toward dynamic reasoning in probabilistic databases with comparable efficiency. We propose a dynamic data s ..."
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Cited by 23 (0 self)
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Traditional databases commonly support efficient query and update procedures that operate in time which is sublinear in the size of the database. Our goal in this paper is to take a first step toward dynamic reasoning in probabilistic databases with comparable efficiency. We propose a dynamic data
Results 1  10
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