This paper proposes a common global variable reassignment and an integrated approach which takes advantage of the complementary relationship of inlining and interprocedural register allocation to reduce the procedure call overhead without causing additional negative effect. Our approach is based on the observation of analyzed program characteristic t o identify the heavy called procedures regio...

We study decompositions of the global NVALUE constraint. Our main contribution is theoretical: we show that there are propagators for global constraints like NVALUE which decomposition can simulate with the same time complexity but with a much greater space complexity. This suggests that the benefit of a global propagator may often not be in saving time but in saving space. Our other theoretica...

One objective of this paper is to estimate the parameters p,d,q of an autoregressive fractionally integrated moving average ARFIMA(p,d,q) stochastic model by minimizing the squares of the residuals using a Bayesian global optimization techniques. We consider bilinear model, too because it is the simple extension of linear model, defined by adding a bilinear term to traditional ARMA model. There...

One challenging issue in sensor networks is to determine where a given sensor node is physically located. This problem is especially crucial for very small sensor nodes. This paper presents a GPS-less, outdoor, self-positioning method for wireless sensor networks. In our method, a set of nodes, called reference points (RPs), are deployed in the sensor network with overlapping regions of coverag...

Multi-objective optimization problem with Lipshitz objective functions is considered. It is shown that the worst-case optimal passive algorithm can be reduced to the computation of centers of balls producing the optimal cover of a feasible region, where the balls are of equal minimum radius. It is also shown, that in the worstcase, adaptivity does not improve the guaranteed accuracy achievable ...

In this paper we compare the average performance of one class of low-discrepancy quasi-Monte Carlo sequences for global optimization. Weiner measure is assumed as the probability prior on all optimized functions. We show how to construct van der Corput sequences and we prove their consistency. Numerical experimentation shows that the van der Corput sequence in base 2 has a better average perfor...

This paper presents a new framework for worst-case toleranced assembly planning of planar mechanical systems. Unlike most assembly planners, which produce plans for nominal parts, our framework incorporates the inherent imprecision of the manufacturing process, which introduces uncertainties in the shape and size of the assembly parts. It accounts for the uncertainty of the relative part placem...

In this paper, we consider magnetic positioning and tracking of objects and provide a comparison of the characteristics of two major measurement models: the magnetic dipole model and the mutual inductance model. The numerical results obtained by applying these models to a short-range position measurement application, with a maximum operating distance of approximately 50 cm, are compared. Based ...

This paper examines worst-case evaluation bounds for finding weak minimizers in unconstrained optimization. For the cubic regularization algorithm, Nesterov and Polyak (2006) [15] and Cartis et al. (2010) [3] show that at most O(ε−3) iterations may have to be performed for finding an iterate which is within ε of satisfying second-order optimality conditions. We first show that this bound can be...