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A QuasiPolynomialTime Algorithm for Sampling Words from a ContextFree Language
, 1995
"... A quasipolynomialtime algorithm is presented for sampling almost uniformly at random from the nslice of the language L(G) generated by an arbitrary contextfree grammar G. (The nslice of a language L over an alphabet \Sigma is the subset L " \Sigma n of words of length exactly n.) The ..."
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Cited by 14 (0 self)
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A quasipolynomialtime algorithm is presented for sampling almost uniformly at random from the nslice of the language L(G) generated by an arbitrary contextfree grammar G. (The nslice of a language L over an alphabet \Sigma is the subset L " \Sigma n of words of length exactly n
An Efficient Implementation of a Quasipolynomial Algorithm for Generating Hypergraph Transversals
, 2003
"... Given a finite set V, and a hypergraph H ⊆ 2V, the hypergraph transversal problem calls for enumerating all minimal hitting sets (transversals) for H. This problem plays an important role in practical applications as many other problems were shown to be polynomially equivalent to it. Fredman and Kh ..."
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Cited by 18 (2 self)
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and Khachiyan (1996) gave an incremental quasipolynomial time algorithm for solving the hypergraph transversal problem [9]. In this paper, we present an efficient implementation of this algorithm. While we show that our implementation achieves the same bound on the running time as in [9], practical experience
Approximation Schemes for Partitioning: Convex Decomposition and Surface Approximation
, 2014
"... We revisit two NPhard geometric partitioning problems – convex decomposition and surface approximation. Building on recent developments in geometric separators, we present quasipolynomial time algorithms for these problems with improved approximation guarantees. ..."
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We revisit two NPhard geometric partitioning problems – convex decomposition and surface approximation. Building on recent developments in geometric separators, we present quasipolynomial time algorithms for these problems with improved approximation guarantees.
A polynomialtime approximation scheme for weighted planar graph TSP
 PROC. 9TH ANNUAL ACMSIAM SYMPOSIUM ON DISCRETE ALGORITHMS, PP 33–41
, 1998
"... Given a planar Rraph on n nodes with costs (weights) on its edges, define;he distance between nodes i &d 2 as ’ the length of the shortest path between i and i. Consider this as &I instance of me & TSP. For any E> 6, our algorithm finds a salesman tour of total cost at most (1 + E) t ..."
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Cited by 61 (13 self)
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) times optimal in time n”(llea). We also present a quasipolynomial time algorithm for the Steiner version of this problem.
Computing the period of an Ehrhart quasipolynomial
 Electron. J. Combin., 12:Research Paper
, 2005
"... If P ⊂ R d is a rational polytope, then iP(t): = #(tP ∩ Z d) is a quasipolynomial in t, called the Ehrhart quasipolynomial of P. A period of iP(t) is D(P), the smallest D ∈ Z+ such that D · P has integral vertices. Often, D(P) is the minimum period of iP(t), but, in several interesting examples, t ..."
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Cited by 6 (0 self)
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If P ⊂ R d is a rational polytope, then iP(t): = #(tP ∩ Z d) is a quasipolynomial in t, called the Ehrhart quasipolynomial of P. A period of iP(t) is D(P), the smallest D ∈ Z+ such that D · P has integral vertices. Often, D(P) is the minimum period of iP(t), but, in several interesting examples
Learning With Many Irrelevant Features
 In Proceedings of the Ninth National Conference on Artificial Intelligence
, 1991
"... In many domains, an appropriate inductive bias is the MINFEATURES bias, which prefers consistent hypotheses definable over as few features as possible. This paper defines and studies this bias. First, it is shown that any learning algorithm implementing the MINFEATURES bias requires \Theta( 1 ff ..."
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Cited by 250 (4 self)
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ffl ln 1 ffi + 1 ffl [2 p + p ln n]) training examples to guarantee PAClearning a concept having p relevant features out of n available features. This bound is only logarithmic in the number of irrelevant features. The paper also presents a quasipolynomial time algorithm, FOCUS, which
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
On the Powers of 2
"... Abstract. In 2013 the function field sieve algorithm for computing discrete logarithms in finite fields of small characteristic underwent a series of dramatic improvements, culminating in the first heuristic quasipolynomial time algorithm, due to Barbulescu, Gaudry, Joux and Thomé. In this article ..."
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Abstract. In 2013 the function field sieve algorithm for computing discrete logarithms in finite fields of small characteristic underwent a series of dramatic improvements, culminating in the first heuristic quasipolynomial time algorithm, due to Barbulescu, Gaudry, Joux and Thomé
Traps to the BGJTAlgorithm for Discrete Logarithms
"... In the recent breakthrough paper by Barbulescu, Gaudry, Joux and Thomé, a quasipolynomial time algorithm (QPA) is proposed for the discrete logarithm problem over finite fields of small characteristic. The time complexity analysis of the algorithm is based on several heuristics presented in their p ..."
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Cited by 14 (2 self)
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In the recent breakthrough paper by Barbulescu, Gaudry, Joux and Thomé, a quasipolynomial time algorithm (QPA) is proposed for the discrete logarithm problem over finite fields of small characteristic. The time complexity analysis of the algorithm is based on several heuristics presented
Results 1  10
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1,969,582