Quaternion versus Rotation Matrix Feedback for Spacecraft Attitude Stabilization Using Magnetorquers

The purpose of this paper is to compare performances between stabilization algorithms of quaternion plus attitude rate feedback and rotation matrix plus attitude rate feedback for an Earth-pointing spacecraft with magnetorquers as the only torque actuators. From a mathematical point of view, an important difference between the two stabilizing laws is that only quaternion feedback can exhibit an undesired behavior known as the unwinding phenomenon. A numerical case study is considered, and two Monte Carlo campaigns are carried out: one in nominal conditions and one in perturbed conditions. It turns out that quaternion feedback compares more favorably in terms of the speed of convergence in both campaigns, and it requires less energy in perturbed conditions.

On Ulam Stability of a Generalized Delayed Differential Equation of Fractional Order

We investigate Ulam stability of a general delayed differential equation of a fractional order. We provide formulas showing how to generate the exact solutions of the equation using functions that satisfy it only approximately. Namely, the approximate solution $$\phi $$ ϕ generates the exact solution as a pointwise limit of the sequence $$\varLambda ^n\phi $$ Λ n ϕ with some integral (possibly, nonlinear) operator $$\varLambda $$ Λ . We estimate the speed of convergence and the distance between those approximate and exact solutions. Moreover, we provide some exemplary calculations, involving the Chebyshev and Bielecki norms and some semigauges, that could help to obtain reasonable outcomes for such estimations in some particular cases. The main tool is the Diaz–Margolis fixed point alternative.

Using particle swarm optimization to solve test functions problems

In this paper the benchmarking functions are used to evaluate and check the particle swarm optimization (PSO) algorithm. However, the functions utilized have two dimension but they selected with different difficulty and with different models. In order to prove capability of PSO, it is compared with genetic algorithm (GA). Hence, the two algorithms are compared in terms of objective functions and the standard deviation. Different runs have been taken to get convincing results and the parameters are chosen properly where the Matlab software is used. Where the suggested algorithm can solve different engineering problems with different dimension and outperform the others in term of accuracy and speed of convergence.

On approximation by random Lüroth expansions

In this paper, we employ a random dynamical systems approach to study generalized Lüroth series expansions of numbers in the unit interval. We prove that for each [Formula: see text] with [Formula: see text] Lebesgue almost all numbers in [Formula: see text] have uncountably many universal generalized Lüroth series expansions with digits less than or equal to [Formula: see text], so expansions in which each possible block of digits occurs. In particular this means that Lebesgue almost all [Formula: see text] have uncountably many universal generalized Lüroth series expansions using finitely many digits only. For [Formula: see text] we show that typically the speed of convergence to an irrational number [Formula: see text] of the corresponding sequence of Lüroth approximants is equal to that of the standard Lüroth approximants. For other rational values of [Formula: see text] we use stationary measures to study the typical speed of convergence of the approximants and the digit frequencies.

A First-Order Optimization Algorithm for Statistical Learning with Hierarchical Sparsity Structure

In many statistical learning problems, it is desired that the optimal solution conform to an a priori known sparsity structure represented by a directed acyclic graph. Inducing such structures by means of convex regularizers requires nonsmooth penalty functions that exploit group overlapping. Our study focuses on evaluating the proximal operator of the latent overlapping group lasso developed by Jacob et al. in 2009. We implemented an alternating direction method of multiplier with a sharing scheme to solve large-scale instances of the underlying optimization problem efficiently. In the absence of strong convexity, global linear convergence of the algorithm is established using the error bound theory. More specifically, the paper contributes to establishing primal and dual error bounds when the nonsmooth component in the objective function does not have a polyhedral epigraph. We also investigate the effect of the graph structure on the speed of convergence of the algorithm. Detailed numerical simulation studies over different graph structures supporting the proposed algorithm and two applications in learning are provided. Summary of Contribution: The paper proposes a computationally efficient optimization algorithm to evaluate the proximal operator of a nonsmooth hierarchical sparsity-inducing regularizer and establishes its convergence properties. The computationally intensive subproblem of the proposed algorithm can be fully parallelized, which allows solving large-scale instances of the underlying problem. Comprehensive numerical simulation studies benchmarking the proposed algorithm against five other methods on the speed of convergence to optimality are provided. Furthermore, performance of the algorithm is demonstrated on two statistical learning applications related to topic modeling and breast cancer classification. The code along with the simulation studies and benchmarks are available on the corresponding author’s GitHub website for evaluation and future use.

Salary Cap, Organizational Gap, and Catch-up in the Performance of NBA Teams: A Two-Stage DEA Model Under Heterogeneity

This paper evaluates National Basketball Association teams performance using a two-stage approach under a metafrontier framework. Our approach decomposes overall efficiency into salary-cap and on-court efficiency. Organizational gaps between different organizational structures are calculated as well. The introduction of the salary cap as an important element for a competitive market preventing monopoly formation allows the examination of the effects on different types of performance. Findings reveal that teams experience increased performance while organizational gaps decline. Moreover, a [Formula: see text] and [Formula: see text] convergence analysis reveals convergence patterns. Finally, the catch-up index denotes that the speed of convergence increases, especially after the salary cap policy implementation.

Testing Of Economic Convergence In The Middle Of The Covid-19 Pandemic

This study aims to see whether the process of export and import convergence occurs with the control variable of the size of the country's economy affected by the COVID-19 pandemic as measured by GDP and spatial interactions. The method of analysis in this study uses the Spatial Dynamic Panel. The research objects are 64 countries affected by the COVID-19 pandemic—data taken in the period 2019q4-2020q3. The results show that GDP and spatial elements influence the process of export and import convergence. The speed of convergence and the length of time to reach a steady-state, imports are better than exports reaching 28-38% with about one semester and reaching 15-16% with a duration of 1 year (export model). Exports and imports during the COVID-19 pandemic reflect interactions between countries, with distance as a determining factor. However, the role of interaction between countries tends to be insignificant in the long run.

Kurtosis-Based Symbol Timing and Carrier Phase/Frequency Tracking

Kurtosis is known to be effective at estimating signal timing and carrier phase offset when the processing is performed in a “burst mode,” that is, operating on a block of received signal in an offline fashion. In this paper, kurtosis-based estimation is extended to provide tracking of timing and carrier phase, and frequency offsets. The algorithm is compared with conventional PLL-type timing/phase estimation and shown to be superior in terms of speed of convergence, with comparable variance in the matched filter output symbols.

Relaxation Limit of the Aggregation Equation with Pointy Potential

This work was devoted to the study of a relaxation limit of the so-called aggregation equation with a pointy potential in one-dimensional space. The aggregation equation is today widely used to model the dynamics of a density of individuals attracting each other through a potential. When this potential is pointy, solutions are known to blow up in final time. For this reason, measure-valued solutions have been defined. In this paper, we investigated an approximation of such measure-valued solutions thanks to a relaxation limit in the spirit of Jin and Xin. We study the convergence of this approximation and give a rigorous estimate of the speed of convergence in one dimension with the Newtonian potential. We also investigated the numerical discretization of this relaxation limit by uniformly accurate schemes.

A probabilistic view on the long-time behaviour of growth-fragmentation semigroups with bounded fragmentation rates

The growth-fragmentation equation models systems of particles that grow and reproduce as time passes. An important question concerns the asymptotic behaviour of its solutions. Bertoin and Watson ( $2018$ ) developed a probabilistic approach relying on the Feynman-Kac formula, that enabled them to answer to this question for sublinear growth rates. This assumption on the growth ensures that microscopic particles remain microscopic. In this work, we go further in the analysis, assuming bounded fragmentations and allowing arbitrarily small particles to reach macroscopic mass in finite time. We establish necessary and sufficient conditions on the coefficients of the equation that ensure Malthusian behaviour with exponential speed of convergence to the asymptotic profile. Furthermore, we provide an explicit expression of the latter.