Results 11 to 20 of about 1,714 (207)
The Intersection of Algorithmically Random Closed Sets and Effective Dimension
ACM Transactions on Computational Logic, 2022 In this article, we study several aspects of the intersections of algorithmically random closed sets. First, we answer a question of Cenzer and Weber, showing that the operation of intersecting relatively random closed sets (random with respect to certain underlying measures induced by Bernoulli measures on the space of codes of closed sets), which ...
Adam Case, Christopher P. Porter
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Performance of global random search algorithms for large dimensions [PDF]
Journal of Global Optimization, 2017 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Andrey Pepelyshev +2 more
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CoRR, 2023 Large language models (LLMs) have shown their power in different areas. Attention computation, as an important subroutine of LLMs, has also attracted interests in theory. Recently the static computation and dynamic maintenance of attention matrix has been studied by [Alman and Song 2023] and [Brand, Song and Zhou 2023] from both algorithmic perspective
Yichuan Deng 0002 +2 more
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Diffusion limits of the random walk Metropolis algorithm in high dimensions [PDF]
The Annals of Applied Probability, 2012 Diffusion limits of MCMC methods in high dimensions provide a useful theoretical tool for studying computational complexity. In particular, they lead directly to precise estimates of the number of steps required to explore the target measure, in stationarity, as a function of the dimension of the state space.
Mattingly, Jonathan C. +2 more
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Journal of Global Optimization, 2023 AbstractWe consider global optimization problems, where the feasible region $${\mathcal {X}}$$ X is a compact subset of $$\mathbb {R}^d$$ R d with $$d \ge 10$$
Jack Noonan, Anatoly Zhigljavsky
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Statistical Estimation in Global Random Search Algorithms in Case of Large Dimensions [PDF]
, 2017 We study asymptotic properties of optimal statistical esti-\ud mators in global random search algorithms when the dimension of the\ud feasible domain is large. The results obtained can be helpful in deciding\ud what sample size is required for achieving a given accuracy of estimation.
Andrey Pepelyshev +2 more
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Performance Analysis of Turbo-Code with Random (and s-random) Interleaver based on 3-Dimension Algorithm [PDF]
The KIPS Transactions:PartA, 2002 In this paper, we apply the 3-dimension algorithm to the random interleaver and s-random interleaver and analyze the performance of the turbo code system with random interleaver (or s-random interleaver). In general, the performance of interleaver is determined by minimum distance between neighbor data, thus we could improve the performance of ...
Hyung-Yun Kong, Ji-Woong Choi
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Dynamic Critical Behavior of the Chayes–Machta Algorithm for the Random-Cluster Model, I. Two Dimensions [PDF]
Journal of Statistical Physics, 2011 LaTeX2e, 75 pages including 26 Postscript ...
Garoni, Timothy M. +3 more
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A Randomized Approximation Convex Hull Algorithm for High Dimensions
IFAC-PapersOnLine, 2015 Abstract The accuracy of classification and regression tasks based on data driven models, such as Neural Networks or Support Vector Machines, relies to a good extent on selecting proper data for designing these models that covers the whole input ranges in which they will be employed. The convex hull algorithm is applied as a method for data selection;
Antonio Ruano +2 more
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Random particle packing with large particle size variations using reduced-dimension algorithms [PDF]
Powder Technology, 2006 We present a reduced-dimension, ballistic deposition, Monte Carlo particle packing algorithm and discuss its application to the analysis of the microstructure of hard-sphere systems with broad particle size distributions. We extend our earlier approach (the ``central string'' algorithm) to a reduced-dimension, quasi-3D approach.
Webb, M. D., Davis, I. L.
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