Maintenance of Soft Calibration Models in the Determination of Zinc,Cadmium, Lead and Copper by Differential Pulse Anodic Stripping Voltammetry

A strategy for constructing a global multivariate calibration model that includes calibration samples measured over time on different days is developed and applied in electroanalysis. Both synthetic and real samples (tap, extracted and river water) are analyzed by differential‐pulse anodic stripping voltammetry, showing the suitability of the global model constructed that provides successful results similar to those of the usual multivariate calibration. In addition the capability of discrimination of this model is evaluated in prediction for the mean of three replicates with estimation of probability of false noncompliance, α, and false compliance, β, being found 3.1, 11.2, 6.7 and 64.7 nM for nominal concentrations of zinc, cadmium, lead and copper of 96.0, 40.4, 37.3 and 328.0 nM respectively when α=β=0.05. It has been proven that the use of the global calibration does not imply a loss of multivariate analytical sensitivity, using this parameter as quality index of the analytical procedure. The viability of using calibration maintenance strategies with electroanalytical techniques is shown, providing a way to save time and experimental effort when these techniques are used in routine analysis.     

Equivalence of two linear programming relaxations for broadcast scheduling

A server needs to compute a broadcast schedule for n pages whose request times are known in advance. Outputting a page satisfies all outstanding requests for the page. The goal is to minimize the average waiting time of a client. In this paper, we show the equivalence of two apparently different relaxations that have been considered for this problem.     

Robust linear optimization under general norms

We explicitly characterize the robust counterpart of a linear programming problem with uncertainty set described by an arbitrary norm. Our approach encompasses several approaches from the literature and provides guarantees for constraint violation under probabilistic models that allow arbitrary dependencies in the distribution of the uncertain coefficients.     

A grid‐free upwind relaxation scheme for inviscid compressible flows

A new grid‐free upwind relaxation scheme for simulating inviscid compressible flows is presented in this paper. The non‐linear conservation equations are converted to linear convection equations with non‐linear source terms by using a relaxation system and its interpretation as a discrete Boltzmann equation. A splitting method is used to separate the convection and relaxation parts. Least squares upwinding is used for discretizing the convection equations, thus developing a grid‐free scheme which can operate on any arbitrary distribution of points. The scheme is grid free in the sense that it works on any arbitrary distribution of points and it does not require any topological information like elements, faces, edges, etc. This method is tested on some standard test cases. To explore the power of the grid‐free scheme, solution‐based adaptation of points is done and the results are presented, which demonstrate the efficiency of the new grid‐free scheme. Copyright © 2005 John Wiley & Sons, Ltd.     

Bounds for the Least Laplacian Eigenvalue of a Signed Graph

A signed graph is a graph with a sign attached to each edge. This paper extends some fundamental concepts of the Laplacian matrices from graphs to signed graphs. In particular, the relationships between the least Laplacian eigenvalue and the unbalancedness of a signed graph are investigated.     

层次分析中广义最小偏差法的性质

广义最小偏差法（GLDM）是层次分析中一种重要的排序方法．本文讨论了广义最小偏差法的性质和灵敏度分析问题．     

The projective method for solving linear matrix inequalities

Numerous problems in control and systems theory can be formulated in terms of linear matrix inequalities (LMI). Since solving an LMI amounts to a convex optimization problem, such formulations are known to be numerically tractable. However, the interest in LMI-based design techniques has really surged with the introduction of efficient interior-point methods for solving LMIs with a polynomial-time complexity. This paper describes one particular method called the Projective Method. Simple geometrical arguments are used to clarify the strategy and convergence mechanism of the Projective algorithm. A complexity analysis is provided, and applications to two generic LMI problems (feasibility and linear objective minimization) are discussed.     

Some Properties of Quasi-keep-range Operators and Their Applications

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On a Question of Phillips

In [5] Phillips proved that one can obtain the additive group of any nonstandard model *? of the ring ? of integers by using a linear mod 1 function h : F ?, where F is the α-dimensional vector space over ? when α is the cardinality of *?. In this connection it arises the question whether there are linear mod 1 functions which are neither addition nor quasi-linear. We prove that this is the case.     

Volumetric path following algorithms for linear programming

We consider the construction of small step path following algorithms using volumetric, and mixed volumetric-logarithmic, barriers. We establish quadratic convergence of a volumetric centering measure using pure Newton steps, enabling us to use relatively standard proof techniques for several subsequently needed results. Using a mixed volumetric-logarithmic barrier we obtain an O(n ^1/4 m ^1/4 L) iteration algorithm for linear programs withn variables andm inequality constraints, providing an alternative derivation for results first obtained by Vaidya and Atkinson. In addition, we show that the same iteration complexity can be attained while holding the work per iteration to O(n ² m), as opposed to O(nm ²), operations, by avoiding use of the true Hessian of the volumetric barrier. Our analysis also provides a simplified proof of self-concordancy of the volumetric and mixed volumetric-logarithmic barriers, originally due to Nesterov and Nemirovskii. This paper was first presented at the 1994 Faculty Research Seminar “Optimization in Theory and Practice”, at the University of Iowa Center for Advanced Studies.