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p. 216-223
Received: 17 May 2010; in revised form: 15 June 2010 / Accepted: 29 June 2010 / Published: 1 July 2010
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| Download PDF Full-text (207 KB) Abstract: This brief note presents an algorithm to solve ordinary stochastic differential equations (SDEs). The algorithm is based on the joint solution of a system of two partial differential equations and provides strong solutions for finite-dimensional systems of SDEs driven by standard Wiener processes and with adapted initial data. Several examples illustrate its use.
p. 224-243
Received: 28 May 2010 / Accepted: 29 June 2010 / Published: 12 July 2010
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| Download PDF Full-text (396 KB) Abstract: The algorithm of Lenstra, Lenstra, and Lovász (LLL) transforms a given integer lattice basis into a reduced basis. Storjohann improved the worst case complexity of LLL algorithms by a factor of O(n) using modular arithmetic. Koy and Schnorr developed a segment-LLL basis reduction algorithm that generates lattice basis satisfying a weaker condition than the LLL reduced basis with O(n) improvement than the LLL algorithm. In this paper we combine Storjohann’s modular arithmetic approach with the segment-LLL approach to further improve the worst case complexity of the segment-LLL algorithms by a factor of n0.5 .
p. 244-254
Received: 8 June 2010; in revised form: 6 July 2010 / Accepted: 8 July 2010 / Published: 15 July 2010
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| Download PDF Full-text (241 KB) Abstract: We analyze further the Magnus-Derek game, a two-player game played on a round table with n positions. The players jointly control the movement of a token. One player, Magnus, aims to maximize the number of positions visited while minimizing the number of rounds. The other player, Derek, attempts to minimize the number of visited positions. We present a new strategy for Magnus that succeeds in visiting the maximal number of positions in 3(n – 1) rounds, which is the optimal number of rounds up to a constant factor.
p. 255-259
Received: 12 March 2010 / Accepted: 14 March 2010 / Published: 20 July 2010
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| Download PDF Full-text (84 KB) Abstract: Ray Solomonoff was always inventive. As a child, he had a lab in his parent's cellar in Cleveland and a secret air hole to vent the smoke from his experiments. He gave his friend Marvin Minsky a so-called "Hurry" clock — a clock labeled "HURRY" that ran very fast. Helped by a friend, he built a year round house in N.H. He put in thick insulation, enabling him to heat the house with two rows of light bulbs along the ceiling. I met Ray shortly after he finished this house, in 1969. I knew about foraging, so I showed him edible plants like Indian Cucumber Root. He was so happy: it was as if we found a fountain of champagne. [...]
p. 260-264
Received: 12 March 2010 / Accepted: 14 March 2010 / Published: 20 July 2010
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| Download PDF Full-text (68 KB) Abstract: Ray J. Solomonoff died on December 7, 2009, in Cambridge, Massachusetts, of complications of a stroke caused by an aneurism in his head. Ray was the first inventor of Algorithmic Information Theory which deals with the shortest effective description length of objects and is commonly designated by the term “Kolmogorov complexity.” In the 1950s Solomonoff was one of the first researchers to treat probabilistic grammars and the associated languages. He treated probabilistic Artificial Intelligence (AI) when “probabilistic” was unfashionable, and treated questions of machine learning early on. But his greatest contribution is the creation of Algorithmic Information Theory. [...]
p. 265-275
Received: 5 May 2010; in revised form: 8 July 2010 / Accepted: 22 July 2010 / Published: 26 July 2010
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| Download PDF Full-text (247 KB) Abstract: The metric average is a binary operation between sets in Rn which is used in the approximation of set-valued functions. We introduce an algorithm that applies tools of computational geometry to the computation of the metric average of 2D sets with piecewise linear boundaries.
p. 276-293
Received: 9 June 2010; in revised form: 11 July 2010 / Accepted: 20 July 2010 / Published: 30 July 2010
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| Download PDF Full-text (150 KB) Abstract: We analytically investigate univariate C1 continuous cubic L1 interpolating splines calculated by minimizing an L1 spline functional based on the second derivative on 5-point windows. Specifically, we link geometric properties of the data points in the windows with linearity, convexity and oscillation properties of the resulting L1 spline. These analytical results provide the basis for a computationally efficient algorithm for calculation of L1 splines on 5-point windows.
p. 294-310
Received: 7 July 2010 / Accepted: 3 August 2010 / Published: 9 August 2010
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| Download PDF Full-text (2254 KB) Abstract: We present a car traffic simulation prototype for complex networks, that is formed by a collection of roads and junctions. Traffic load evolution is described by a model based on fluid dynamic conservation laws, deduced from conservation of the number of cars. The model contains some additional hypothesis in order to reproduce specific car traffic features such as route based car distribution at nodes and the presence of right-of-way at the crossroads. A complete implementation of this model is then presented, together with computational results on case studies.
p. 311-328
Received: 11 July 2010 / Accepted: 10 August 2010 / Published: 20 August 2010
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| Download PDF Full-text (327 KB) Abstract: We compare univariate L1 interpolating splines calculated on 5-point windows, on 7-point windows and on global data sets using four different spline functionals, namely, ones based on the second derivative, the first derivative, the function value and the antiderivative. Computational results indicate that second-derivative-based 5-point-window L1 splines preserve shape as well as or better than the other types of L1 splines. To calculate second-derivative-based 5-point-window L1 splines, we introduce an analysis-based, parallelizable algorithm. This algorithm is orders of magnitude faster than the previously widely used primal affine algorithm.
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