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Abstract. We introduce and study a randomized quasi-Monte Carlo method for estimating the state distribution at each step of a Markov chain. The number of steps in the chain can be random and unbounded. The method simulates n copies of the chain in parallel, using a (d + 1)-dimensional highly-uniform point set of cardinality n, randomized independently at each step, where d is the number of uniform random numbers required at each transition of the Markov chain. This technique is effective in particular to obtain a low-variance unbiased estimator of the expected total cost up to some random stopping time, when state-dependent costs are paid at each step. It is generally more effective when the state space has a natural order related to the cost function. We provide numerical illustrations where the variance reduction with respect to standard Monte Carlo is substantial. The variance can be reduced by factors of several thousands in some cases. We prove bounds on the convergence rate of the worst-case error and variance for special situations. In line with what is typically observed in randomized quasi-Monte Carlo contexts, our empirical results indicate much better convergence than what these bounds guarantee.

Quasi-Monte Carlo (QMC) methods have begun to displace ordinary Monte Carlo (MC) methods in many practical problems. It is natural and obvious to combine QMC methods with traditional variance reduction techniques used in MC sampling, such as control variates. There can,

The selection criteria we propose are deduced from a recent study in one dimension [Fau05] and may be applied to each one-dimensional coordinate projection of any digital (0,s)-sequence in prime base [Fau82] (or even digital (t, s)-sequence in prime base [Nie92]).

We study the performance in practice of various point-location algorithms implemented in Cgal, including a newly devised Landmarks algorithm. Among the other algorithms studied are: a naïve approach, a “walk along a line ” strategy and a trapezoidal-decomposition based search structure. The current implementation addresses general arrangements of arbitrary planar curves, including arrangements of non-linear segments (e.g., conic arcs) and allows for degenerate input (for example, more than two curves intersecting in a single point, or overlapping curves). All calculations use exact number types and thus result in the correct point location. In our Landmarks algorithm (a.k.a. Jump & Walk), special points, “landmarks”, are chosen in a preprocessing stage, their place in the arrangement is found, and they are inserted into a data-structure that enables efficient nearest-neighbor search. Given a query point, the nearest landmark is located and then the algorithm “walks ” from the landmark to the query point. We report on extensive experiments with arrangements composed of line segments or conic arcs. The results indicate that the Landmarks approach is the most efficient when the overall cost of a query is taken into account, combining both preprocessing and query time. The simplicity of the algorithm enables an almost straightforward implementation and rather easy maintenance. The generic programming implementation allows versatility both in the selected type of landmarks, and in the choice of the nearest-neighbor search structure. The end result is a highly effective point-location algorithm for most practical purposes. ∗ Work reported in this paper has been supported in part by the IST Programme of the EU as a Shared-corst RTD

In the present paper we introduce and study finite point subsets of a special kind, called optimum distributions, in the n-dimensional unit cube. Such distributions are close related with known (#, s, n)-nets of low discrepancy. It turns out that optimum distributions have a rich combinatorial structure. Namely, we show that optimum distributions can be characterized completely as maximum distance separable codes with respect to a non-Hamming metric. Weight spectra of such codes can be evaluated precisely. We also consider linear codes and distributions and study their general properties including the duality with respect to a suitable inner product. The corresponding generalized MacWilliams identies for weight enumerators are brifly discussed. Broad classes of linear maximum distance separable codes and linear optimum distributions are explicitly constructed in the paper by the Hermite interpolations over finite fields. 1991 Mathematics Subject Classification. 11K38, 11T71, 94B60 Key...

Discrepancy theory investigates how uniform nonrandom structures can be. For example, given n points in the plane, how should we color them red and blue so as to minimize the difference between the number of red points and the number of blue ones within any disk? Or, how should we place n points in the unit square

AIChE shall not be responsible for statements or opinions contained in papers or printed in publications. The design of constrained, “plant-friendly ” multisine input signals that optimize a geometric discrepancy criterion arising from Weyl’s Theorem is examined in this paper. Such signals are meaningful for data-centric estimation methods, where uniform coverage of the output state-space is critical. The usefulness of this problem formulation is demonstrated by applying it to a linear example and to the nonlinear, highly interactive distillation column model developed by Weischedel and McAvoy (1980). The optimization problem includes a search for both the Fourier coefficients and phases in the multisine signal, resulting in an uniformly distributed output signal displaying a desirable balance between high and low gain directions. The solution involves very little user intervention (which enhances its practical usefulness) and has significant benefits compared to multisine signals that minimize crest factor. The effectiveness of data resulting from a Weyl criterion-based signal for Model-on-Demand Model Predictive Control (a data-centric multivariable control algorithm) is demonstrated for the distillation column case study.

Consider the plane as a checkerboard, with each unit square colored black or white in an arbitrary manner. We show that for any such coloring there are straight line segments, of arbitrarily large length, such that the difference of their white length minus their black length, in absolute value, is at least the square root of their length, up to a multiplicative constant. For the corresponding “finite ” problem (N ×N checkerboard) we also prove that we can color it in such a way that the above quantity is at most C √ N log N, for any placement of the line segment. 1

Abstract not found