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As a basis for meaningful simulation and optimization efforts with regard to traffic engineering or energy consumption in telecommunication networks, suitable models are indispensable. This concerns not only realistic network topologies but also models for the geographical distribution and the temporal dynamics of traffic, as well as the assumptions on network components and technology. This paper derives such a model from the practice of a large national carrier. Applying the network and traffic model, we demonstrate its use by presenting various optimization cases related to energy-efficient telecommunication. Here, we focus on load adaptivity by employing sleep modes to the network hardware, where several constraints on the reconfigurability of the network over time are considered.
As a basis for meaningful simulation and optimization efforts with regard to traffic engineering or energy consumption in telecommunication networks, suitable models are indispensable. This concerns not only realistic network topologies, but also models for the geographical distribution and the temporal dynamics of traffic, as well as the assumptions on network components and technology. This paper derives such a model from the practice of a large national carrier.
Applying the network and traffic model, we demonstrate its use by presenting various optimization cases related to energy-efficient telecommunication. Here, we focus on load-adaptivity by employing sleep modes to the network hardware, where several constraints on the reconfigurability of the network over time are considered.
Electrospray ionization-ion mobility spectrometry was employed for the determination of collision cross sections (CCS) of 25 synthetically produced peptides in the mass range between 540–3310 Da. The experimental measurement of the CCS is complemented by their calculation applying two different methods. One prediction method is the intrinsic size parameter (ISP) method developed by the Clemmer group. The second new method is based on the evaluation of molecular dynamics (MD) simulation trajectories as a whole, resulting in a single, averaged collision cross-section value for a given peptide in the gas phase. A high temperature MD simulation is run in order to scan through the whole conformational space. The lower temperature conformational distribution is obtained through thermodynamic reweighting. In the first part, various correlations, e.g. CCS vs. mass and inverse mobility vs. m/z correlations, are presented. Differences in CCS between peptides are also discussed in terms of their respective mass and m/z differences, as well as their respective structures. In the second part, measured and calculated CCS are compared. The agreement between the prediction results and the experimental values is in the same range for both calculation methods. While the calculation effort of the ISP method is much lower, the MD method comprises several tools providing deeper insights into the conformations of peptides. Advantages and limitations of both methods are discussed. Based on the separation of two pairs of linear and cyclic peptides of virtually the same mass, the influence of the structure on the cross sections is discussed. The shift in cross section differences and peak shape after transition from the linear to the cyclic peptide can be well understood by applying different MD tools, e.g. the root-mean-square deviation (RMSD) and the root mean square fluctuation (RMSF).
In this article we analyse a generalized trapezoidal rule for initial value problems with piecewise smooth right-hand side F : IR^n -> IR^n based on a generalization of algorithmic differentiation. When applied to such a problem, the classical trapezoidal rule suffers from a loss of accuracy if the solution trajectory intersects a nondifferentiability of F. The advantage of the proposed generalized trapezoidal rule is threefold: Firstly, we can achieve a higher convergence order than with the classical method. Moreover, the method is energy preserving for piecewise linear Hamiltonian systems. Finally, in analogy to the classical case we derive a third-order interpolation polynomial for the numerical trajectory. In the smooth case, the generalized rule reduces to the classical one. Hence, it is a proper extension of the classical theory. An error estimator is given and numerical results are presented.
It is shown how piecewise differentiable functions F : IR^n -> IR^m that are defined by evaluation programmes can be approximated locally by a piecewise linear model based on a pair of sample points \check x and \hat x. We show that the discrepancy between function and model at any point x is of the bilinear order O(||x - \check x||*||x - \hat x||). As an application of the piecewise linearization procedure we devise a generalized Newton's method based on successive piecewise linearization and prove for it sufficient conditions for convergence and convergence rates equalling those of semismooth Newton. We conclude with the derivation of formulas for the numerically stable implementation of the aforedeveloped piecewise linearization methods.
In this article we analyze a generalized trapezoidal rule for initial value problems with piecewise smooth right hand side F:IR^n -> IR^n.
When applied to such a problem, the classical trapezoidal rule suffers from a loss of accuracy if the solution trajectory intersects a non-differentiability of F. In such a situation the investigated generalized trapezoidal rule achieves a higher convergence order than the classical method. While the asymptotic behavior of the generalized method was investigated in a previous work, in the present article we develop the algorithmic structure for efficient implementation strategies
and estimate the actual computational cost of the latter.
Moreover, energy preservation of the generalized trapezoidal rule is proved for Hamiltonian systems with piecewise linear right hand side.
Recent research has shown that piecewise smooth (PS) functions can be approximated by piecewise linear functions with second order error in the distance to
a given reference point. A semismooth Newton type algorithm based on successive application of these piecewise linearizations was subsequently developed
for the solution of PS equation systems. For local bijectivity of the linearization
at a root, a radius of quadratic convergence was explicitly calculated in terms
of local Lipschitz constants of the underlying PS function. In the present work
we relax the criterium of local bijectivity of the linearization to local openness.
For this purpose a weak implicit function theorem is proved via local mapping
degree theory. It is shown that there exist PS functions f:IR^2 --> IR^2 satisfying the weaker
criterium where every neighborhood of the root of f contains a point x such that
all elements of the Clarke Jacobian at x are singular. In such neighborhoods
the steps of classical semismooth Newton are not defined, which establishes
the new method as an independent algorithm. To further clarify the relation between a PS function and its piecewise linearization,
several statements about structure correspondences between the two are proved.
Moreover, the influence of the specific representation of the local piecewise linear models
on the robustness of our method is studied.
An example application from cardiovascular mathematics is given.
It is shown how piecewise differentiable functions \(F: R^n → R^m\) that are defined by evaluation programs can be approximated locally by a piecewise linear model based on a pair of sample points x̌ and x̂. We show that the discrepancy between function and model at any point x is of the bilinear order O(||x − x̌|| ||x − x̂||). This is a little surprising since x ∈ R^n may vary over the whole Euclidean space, and we utilize only two function samples F̌ = F(x̌) and F̂ = F(x̂), as well as the intermediates computed during their evaluation. As an application of the piecewise linearization procedure we devise a generalized Newton’s method based on successive piecewise linearization and prove for it sufficient conditions for convergence and convergence rates equaling those of semismooth Newton. We conclude with the derivation of formulas for the numerically stable implementation of the aforedeveloped piecewise linearization methods.
In this article we analyze a generalized trapezoidal rule for initial value problems with piecewise smooth right hand side \(F:R^n \to R^n\) based on a generalization of algorithmic differentiation. When applied to such a problem, the classical trapezoidal rule suffers from a loss of accuracy if the solution trajectory intersects a nondifferentiability of \(F\). The advantage of the proposed generalized trapezoidal rule is threefold: Firstly, we can achieve a higher convergence order than with the classical method. Moreover, the method is energy preserving for piecewise linear Hamiltonian systems. Finally, in analogy to the classical case we derive a third order interpolation polynomial for the numerical trajectory. In the smooth case the generalized rule reduces to the classical one. Hence, it is a proper extension of the classical theory. An error estimator is given and numerical results are presented.