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Algorithms for the shortest and closest lattice vector problems
 In Yeow Meng Chee, Zhenbo Guo, San Ling, Fengjing Shao, Yuansheng Tang, Huaxiong Wang, and Chaoping Xing, editors, IWCC, volume 6639 of Lecture Notes in Computer Science
"... Abstract. We present the state of the art solvers of the Shortest and Closest Lattice Vector Problems in the Euclidean norm. We recall the three main families of algorithms for these problems, namely the algorithm by Micciancio and Voulgaris based on the Voronoi cell [STOC’10], the MonteCarlo algor ..."
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Cited by 23 (0 self)
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Abstract. We present the state of the art solvers of the Shortest and Closest Lattice Vector Problems in the Euclidean norm. We recall the three main families of algorithms for these problems, namely the algorithm by Micciancio and Voulgaris based on the Voronoi cell [STOC’10], the Monte
Enumerative Algorithms for the Shortest and Closest Lattice Vector Problems in Any Norm via MEllipsoid Coverings
, 2010
"... We give an algorithm for solving the exact Shortest Vector Problem in ndimensional lattices, in any norm, in deterministic 2 O(n) time (and space), given poly(n)sized advice that depends only on the norm. In many norms of interest, including all ℓp norms, the advice is efficiently and deterministi ..."
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Cited by 5 (1 self)
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We give an algorithm for solving the exact Shortest Vector Problem in ndimensional lattices, in any norm, in deterministic 2 O(n) time (and space), given poly(n)sized advice that depends only on the norm. In many norms of interest, including all ℓp norms, the advice is efficiently
Closest Point Search in Lattices
 IEEE TRANS. INFORM. THEORY
, 2000
"... In this semitutorial paper, a comprehensive survey of closestpoint search methods for lattices without a regular structure is presented. The existing search strategies are described in a unified framework, and differences between them are elucidated. An efficient closestpoint search algorithm, ba ..."
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Cited by 324 (2 self)
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problems for lattices, such as finding a shortest vector, determining the kissing number, compu...
Planning Algorithms
, 2004
"... This book presents a unified treatment of many different kinds of planning algorithms. The subject lies at the crossroads between robotics, control theory, artificial intelligence, algorithms, and computer graphics. The particular subjects covered include motion planning, discrete planning, planning ..."
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Cited by 1108 (51 self)
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This book presents a unified treatment of many different kinds of planning algorithms. The subject lies at the crossroads between robotics, control theory, artificial intelligence, algorithms, and computer graphics. The particular subjects covered include motion planning, discrete planning
Approximating Shortest Lattice Vectors is Not Harder Than Approximating Closest Lattice Vectors
"... We show that given oracle access to a subroutine which returns approximate closest vectors in a lattice, one may nd in polynomial time approximate shortest vectors in a lattice. The level of approximation is maintained; that is, for any function f , the following holds: Suppose that the subroutine, ..."
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Cited by 56 (11 self)
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, and preserves the dimension of the lattice, i.e. the closest vector oracle is called on lattices of exactly the same dimension as the original shortest vector problem. This result establishes the widely believed conjecture by which the shortest vector problem is not harder than the closest vector problem
Linear pattern matching algorithms
 IN PROCEEDINGS OF THE 14TH ANNUAL IEEE SYMPOSIUM ON SWITCHING AND AUTOMATA THEORY. IEEE
, 1972
"... In 1970, Knuth, Pratt, and Morris [1] showed how to do basic pattern matching in linear time. Related problems, such as those discussed in [4], have previously been solved by efficient but suboptimal algorithms. In this paper, we introduce an interesting data structure called a bitree. A linear ti ..."
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Cited by 549 (0 self)
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In 1970, Knuth, Pratt, and Morris [1] showed how to do basic pattern matching in linear time. Related problems, such as those discussed in [4], have previously been solved by efficient but suboptimal algorithms. In this paper, we introduce an interesting data structure called a bitree. A linear
An Efficient Boosting Algorithm for Combining Preferences
, 1999
"... The problem of combining preferences arises in several applications, such as combining the results of different search engines. This work describes an efficient algorithm for combining multiple preferences. We first give a formal framework for the problem. We then describe and analyze a new boosting ..."
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Cited by 707 (18 self)
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The problem of combining preferences arises in several applications, such as combining the results of different search engines. This work describes an efficient algorithm for combining multiple preferences. We first give a formal framework for the problem. We then describe and analyze a new
Solving shortest and closest vector problems: The decomposition approach
"... Abstract. In this paper, we present a heuristic algorithm for solving exact, as well as approximate, SVP and CVP for lattices. This algorithm is based on a new approach which is very different from and complementary to the sieving technique. This new approach frees us from the kissing number bound a ..."
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Cited by 4 (1 self)
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and allows us to solve SVP and CVP in lattices of dimension n in time 2 0.377 n using memory 2 0.292 n. The key idea is to no longer work with a single lattice but to move the problems around in a tower of related lattices. We initiate the algorithm by sampling very short vectors in a dense overlattice
Support vector machine active learning for image retrieval
, 2001
"... Relevance feedback is often a critical component when designing image databases. With these databases it is difficult to specify queries directly and explicitly. Relevance feedback interactively determinines a user’s desired output or query concept by asking the user whether certain proposed images ..."
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Cited by 448 (29 self)
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are relevant or not. For a relevance feedback algorithm to be effective, it must grasp a user’s query concept accurately and quickly, while also only asking the user to label a small number of images. We propose the use of a support vector machine active learning algorithm for conducting effective relevance
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