Inequality Constrained Research Articles

Critical Feminist Approaches to Migration and Mobility Justice in Canada: Guest Editors' Introduction

<p>This themed section examines how inequality constrain and enable contemporary human movement at state border crossings. It responds directly to questions, practices, and knowledge gaps that arise from critical migration/refugee studies, critical tourism studies, border studies, and/or mobility justice research by denaturalizing assumptions about the rights of some to choose to move across borders freely and others who are forced to leave, denied access, or detained. In so doing, we highlight research on human mobilities and borders in Canada to advance understandings on the dynamics of territorial control and access to state borders. What links the articles is the commitment to examining the reproduction of power through the following three shared understandings and starting points: (1) a critique of the Canadian nation state’s global reputation as exceptionally humanitarian (Nguyen and Phu 2021); (2) a consideration of the global entanglements of racial capitalism and colonialism that structures human movement (Gutiérrez Rodríguez 2018); and (3) an understanding that the mobilities of some and the immobilities of others coexist and are in fact co-produced (Ahmed et al. 2020; Bauman 1998; Sheller 2018).</p><p> </p>

An outlier detection and recovery method based on moving least squares quasi-interpolation scheme and [formula omitted][formula omitted]-minimization problem

In the scattered data used for function approximation, there may be some data that deviate greatly from their true values, which are called outliers. The existence of outliers significantly affects the accuracy of some approximation methods, such as moving least squares. How to quickly and accurately find these outliers and restore their true values has not been well resolved. In this paper, a new outlier detection and recovery method is proposed for data processing. The method uses the high accuracy of moving least squares quasi-interpolation scheme, its sensitivity to outliers and the sparse distribution of outliers to construct a ł0-minimization problem with an inequality constrain. Under certain assumptions, it is proved theoretically that the deviation vector corresponding to the outliers is a solution to the optimization problem. The classical orthogonal matching pursuit algorithm is introduced to solve the optimization problem efficiently. By solving the problem, the outliers are marked, and the deviations are also estimated approximately, which can restore the true values of outliers. The numerical experiments demonstrate that the proposed method has high computational efficiency, very high detection accuracy, and high recovery accuracy for the scattered data used for function approximation, so it is practical.

Leggett–Garg-like Inequalities from a Correlation Matrix Construction

The Leggett–Garg Inequality (LGI) constrains, under certain fundamental assumptions, the correlations between measurements of a quantity Q at different times. Here, we analyze the LGI and propose similar but somewhat more elaborate inequalities, employing a technique that utilizes the mathematical properties of correlation matrices, which was recently proposed in the context of nonlocal correlations. We also find that this technique can be applied to inequalities that combine correlations between different times (as in LGI) and correlations between different locations (as in Bell inequalities). All the proposed bounds include additional correlations compared to the original ones and also lead to a particular form of complementarity. A possible experimental realization and some applications are briefly discussed.

A SSLE-Type Algorithm of Quasi-Strongly Sub-Feasible Directions for Inequality Constrained Minimax Problems

A SSLE-Type Algorithm of Quasi-Strongly Sub-Feasible Directions for Inequality Constrained Minimax Problems

Tropical algebra for noise removal and optimal control

Algorithms for noise removal are either complex or ineffective, and the optimal control with inequality constrains makes the algorithm even more complex. Now the condition is changed completely, and the tropical algebra is an extremely simple tool for this purpose. The tropical algebra-based filter has obvious advantages over traditional ones, and an inequality constrain can be converted to a tropical polynomial, making the tropical algebra much attractive in engineering applications. In this paper, idempotents on multiplicative semigroups of tropical matrices are studied. First, the concepts of tropical algebra and the semigroup of tropical matrices under multiplication are introduced. Second, the structure of idempotents on the semigroup of [Formula: see text] tropical matrices under multiplication is given. Finally, as examples, the tropical addition is used as a filter for noise removal, and a simple optimization is given where the constraint is given by the tropical algebra. The paper has opened the path for a new way to noise removal and optimization.

Combining Cross-Entropy and MADS Methods for Inequality Constrained Global Optimization

This paper proposes a way to combine the Mesh Adaptive Direct Search (MADS) algorithm with the Cross-Entropy (CE) method for nonsmooth constrained optimization. The CE method is used as an exploration step by the MADS algorithm. The result of this combination retains the convergence properties of MADS and allows an efficient exploration in order to move away from local minima. The CE method samples trial points according to a multivariate normal distribution whose mean and standard deviation are calculated from the best points found so far. Numerical experiments show the efficiency of this method compared to other global optimization heuristics. Moreover, applied on complex engineering test problems, this method allows an important improvement to reach the feasible region and to escape local minima.

Does Economic Inequality Constrain Intergenerational Economic Mobility? The Association Between Income Inequality During Childhood and Intergenerational Income Persistence in the United States

Using the National Longitudinal Study of Youth 1979, I examine the association between income inequality and intergenerational income mobility in the United States. This study finds that rising income inequality is associated with strengthening the importance of parental family income to child’s income. Particularly, the evidence that greater income inequality decreases intergenerational income mobility is clearer when interstate migration problems are addressed. This evidence indicates that income inequality matters since it hinders the equal opportunity to succeed, especially for children from low-income families. If equality of opportunity is a value for policymakers, it provides justification for policy interventions and government efforts to reduce income inequality. A number of sensitivity tests confirm that the main results are robust and reliable.

A regression-based improvement to the multiple criteria ABC inventory classification analysis

The aim of this paper is to propose a regression-based approach for obtaining a set of weights for multi-criteria ABC inventory analysis, which differ across classification criteria but are common across inventory items and follow a predetermined descending ordering scheme regarding the relative importance of classification criteria. The proposed alternative is based on the Inequality Constrained Least Squared model and is to be compared with the existing linear and non-linear programming models available in the literature.

Solving Unit Commitment Problem in Deregulated Power Systems Environment. (Dept. E. )

With the fast-paced changing technologies in the power industry, new power references addressing new technologies are coming to the market. So there is an urgent need to keep track of international experiences and activities taking place in the field of modern unit-commitment (UC) problem. UC is a nonlinear mixed integer optimization problem to schedule the operation of the generating units at minimum operating cost while satisfying the demand and other equality and inequality constrains. The UC problem has to determine the on/off state of the generating units at each hour of the planning period and optimally dispatch the load among the committed units. UC is the most significant optimization task in the operation of the power systems. Solving the UC problem for large power systems is computationally expensive. The complexity of the UC problems grows exponentially with the number of generating units especially by applying the deregulated rules in power system. Where in this environment the objective function is maximizing the profit while satisfying the regular unit commitment constrains in addition to new constrains such as bilateral and multilateral contracts. So in this paper, an exact mathematical optimization procedure called “dynamic programming.” is presented to solve of the UC problem in the deregulated environment. The proposed algorithm is implemented in MATLAB environment.

A Comparison of Normal Cone Conditions for Homotopy Methods for Solving Inequality Constrained Nonlinear Programming Problems

Homotopy methods are powerful tools for solving nonlinear programming. Their global convergence can be generally established under conditions of the nonemptiness and boundness of the interior of the feasible set, the Positive Linear Independent Constraint Qualification (PLICQ), which is equivalent to the Mangasarian-Fromovitz Constraint Qualification (MFCQ), and the normal cone condition. This paper provides a comparison of the existing normal cone conditions used in homotopy methods for solving inequality constrained nonlinear programming.

Critical Clearing Time Sensitivity for Inequality Constrained Systems

With the growth of renewable generation (RG) and the development of associated ride through curves serving as operating limits, during disturbances, on violation of these limits, the power system is at risk of losing large amounts of generation. In order to identify preventive control measures that avoid such scenarios from manifesting, the power system must be modeled as a constrained dynamical system. For such systems, the interplay of feasibility region (man-made limits) and stability region (natural dynamical system response) results in a positively invariant region in state space known as the constrained stability region (CSR). After the occurrence of a disturbance, as it is desirable for the system trajectory to lie within the CSR, critical clearing time (CCT) must be defined with respect to the CSR instead of the stability region as is done traditionally. The sensitivity of CCT to system parameters of constrained systems then becomes beneficial for planning/revising protection settings (which impact feasible region) and/or operation (which impact dynamics). In this paper, we derive the first order CCT sensitivity of generic constrained power systems using the efficient power system trajectory sensitivity computation, pioneered by Hiskens and Pai in [“Trajectory sensitivity analysis of hybrid systems,” IEEE Trans. Circuits Syst. Fundam. Theory Appl., vol. 47, no. 2, pp. 204-220, Feb. 2000]. The results are illustrated for a single-machine infinite-bus (SMIB) system as well as a multi-machine system in order to gain meaningful insight into the dependence between ability to meet constraints, system stability, and changes occurring in power system parameters, such as, mechanical power input and inertia.

Optimization Approach for Image Forming in Ultrasound Transmission Tomography (UTT): Real Data Case

Effective image reconstruction algorithm that could be used in Ultrasound Transmission Tomography (UTT) is presented in this paper. The reconstruction process was bringing over to the solution of the inverse problem which was converted to the optimization task. The optimization parameters were selected as follows: a position vector defining the position of the obstacle centre (the length of the position vector and its angle) and the internal radius of the test objects (obstacles). In this case, sixteen trial objects were selected, which gives 48th optimization parameters (three parameters for each trial object). During the optimization process some of them could diminish to zero; the others would change the position and dimensions in such a way that the calculated signal become as close as possible to the measured one. The objective function and linear inequality constrain were defined. The optimization process is very sensitive how far away from the optimal solution the starting point was defined. That is why three cases of the selection of the starting point to the optimization process were considered.

Unscented Recursive Filtering for Inequality Constrained Systems

Unscented Recursive Filtering for Inequality Constrained Systems

Simultaneous Tensor Completion and Denoising by Noise Inequality Constrained Convex Optimization

Convex optimization, rather than a non-convex approach, still play important roles in many computer science applications because of its exactness and efficiency. In this paper, we consider a tensor completion problem with noise based on the convex optimization. When we assume noisy entries, the optimization problem is usually considered as the “regularization” problem, which simultaneously minimizes penalty and error terms with some tradeoff parameters. However, the good value of the tradeoff is not easily determined because of the differences between the two units and the dependency on data. From the perspective of trade-off tuning, the noisy tensor completion problem with the “noise inequality constraint” is preferred than the “regularization,” because the good noise threshold can be easily bound with noise standard deviation. In this paper, we attempt to solve convex tensor completion problems by using two types of noise inequality constraints: Gaussian and Laplace distributions. To solve the inequality constrained convex optimization in a direct way, we derived the proximal mappings for noise inequalities that are analytically computable with low computational complexity. The optimization algorithm is developed based on the primal-dual splitting framework, and a new step-size adaptation method is proposed to accelerate the optimization. The extensive experiments are conducted to demonstrate the advantages of the proposed method for retrieval of visual data, such as color images, movies, and 3D-volumetric data.

Fuzzy Proportion and Integral Control of Synchronous Generator

A fuzzy proportion plus integral (FPI) control strategy is investigated for the automatic voltage regulator (AVR) of synchronous generators (SGs). The Takagi-Sugeno·(T-S) fuzzy state observer is employed to solve the SGs state estimation over its full operating range. Based on the obtained observer, an optimal PI controller is proposed to control over the excitation system of a SG. Then, the controller design is formulated as a H2 optimization problem, which can be transformed into eigenvalue problems (EVPs) with linear matrix inequality (LMI) constrains. The EVP is a convex optimization, which can be tackled easily by using MATLAB/LMI toolbox. Simulation results show the effectiveness of the proposed controller.

Strong Stability Preserving General Linear Methods with Runge–Kutta Stability

We investigate strong stability preserving (SSP) general linear methods (GLMs) for systems of ordinary differential equations. Such methods are obtained by the solution of the minimization problems with nonlinear inequality constrains, corresponding to the SSP property of these methods, and equality constrains, corresponding to order and stage order conditions. These minimization problems were solved by the sequential quadratic programming algorithm implemented in MATLAB $$^{\circledR }$$ subroutine fmincon.m starting with many random guesses. Examples of transformed SSP GLMs of order $$p = 1, 2, 3$$ , and 4, and stage order $$q = p$$ have been determined, and suitable starting and finishing procedures have been constructed. The numerical experiments performed on a set of test problems have shown that transformed SSP GLMs constructed in this paper are more accurate than transformed SSP DIMSIMs and SSP Runge–Kutta methods of the same order.

Mathematical modeling and optimization of cam mechanism in delivery system of an offset press

This paper presents a new method for cam profile design and optimization by integrating a single objective optimization procedure with a dynamic model of cam-follower mechanism in delivery system of an offset press. The proposed approach based on a new strategy mainly includes two parts, a single objective optimization procedure for the kinematic characteristics of the follower and a dynamic model for the cam-follower mechanism. For the first part, the follower acceleration profile is described by modified trapezoidal curve, thereby deriving the velocity, jerk and displacement functions, respectively. Then sequential quadratic programming (SQP), which deals well with equality and inequality constrains, is adopted as the single objective optimized algorithm in this part. In the second part, a two degrees of freedom lumped parameter model taking into account the torsional flexibility and damping of the grippers, the contact compliance at the cam/roller and grippers/block interfaces, is established to investigate the dynamic behavior of the force-closed cam-follower mechanism. In order to improve the kinematic and dynamic characteristics of cam-follower mechanism synthetically, an optimization cycle, known as iterative process, is introduced to implement the procedure of single objective optimization and dynamic simulation alternately. The example illustrated in this paper, sufficiently demonstrates the effectiveness of the new method. Ultimately, the proposed approach gives cam designers a useful tool to design and optimize the cam profile, so as to reduce residual vibration.

Sampled-Data Fuzzy Stabilization of Nonlinear Systems Under Nonuniform Sampling

This paper investigates the sampled-data fuzzy stabilization problem for a class of nonlinear systems that is exactly modeled in Takagi–Sugeno fuzzy form at least locally. A new method for designing parallel distribution compensation fuzzy controller is proposed, which just requires that the nonlinear function is locally Lipschitz. By considering the sample-and-hold behavior of the system and using Jensen's integral inequality, an inequality constrain condition is derived from the locally Lipschitz property. Further, by defining a time-dependent Lyapunov–Krasovskii functional term, a new technique instead of the use of S-procedure is developed, and stabilization conditions for state feedback and observer-based output feedback under nonuniform sampling are obtained. Compared with the existing ones, the new design method not only avoids the difficulty of finding exact upper bounds of asynchronous errors of mismatch membership functions, but contains less conservatism and less numerical complexity as well. Finally, some illustrative examples are given to show the effectiveness of the proposed design method and the significant improvement over the existing results.

Cam profile optimization for the delivery system of an offset press

In this paper, a relatively new strategy known as unified optimization is applied to the cam profile optimization for the delivery system of an offset press by integrating the procedure of single objective optimization with ADAMS software. The proposed approach mainly consists of two parts that includes a single objective optimization procedure and a multibody dynamic model of cam–follower mechanism. For the procedure of single objective optimization, the design process starts from defining the follower acceleration profile by using a modified trapezoidal curve, then genetic algorithm is adopted to determine the evaluating indexes for the kinematic behavior of cam–follower mechanism in multiobjective optimization. Subsequently, sequential quadratic programming, which deals well with equality and inequality constrains, is selected as single objective optimized algorithm in this part. On the other hand, the dynamic simulation developed by ADAMS software is carried out to investigate the dynamic characteristics of cam–follower mechanism. Finally, an optimization cycle, also known as iterative process, is proposed to implement the procedure of single objective optimization and dynamic simulation alternately to improve the kinematic and dynamic characteristics of cam–follower mechanism fully. The cam profile optimization method presented in this paper provides a new tool for cam designers to avoid the undesirable impact and follower jump.

New Facts in Regression Estimation under Conditions of Multicollinearity

This paper considers the approaches and methods for reducing the influence of multi-collinearity. Great attention is paid to the question of using shrinkage estimators for this purpose. Two classes of regression models are investigated, the first of which corresponds to systems with a negative feedback, while the second class presents systems without the feedback. In the first case the use of shrinkage estimators, especially the Principal Component estimator, is inappropriate but is possible in the second case with the right choice of the regularization parameter or of the number of principal components included in the regression model. This fact is substantiated by the study of the distribution of the random variable , where b is the LS estimate and β is the true coefficient, since the form of this distribution is the basic characteristic of the specified classes. For this study, a regression approximation of the distribution of the event based on the Edgeworth series was developed. Also, alternative approaches are examined to resolve the multicollinearity issue, including an application of the known Inequality Constrained Least Squares method and the Dual estimator method proposed by the author. It is shown that with a priori information the Euclidean distance between the estimates and the true coefficients can be significantly reduced.