I Going Away. I Going Home. : Austin Clarke\u27s Leaving this Island Place

Austin Clarke’s “Leaving This Island Place” is one of scores of Caribbean autobiographical works that focus on a bright, young, lower-class islander leaving his/her small island place and setting out on “Eldorado voyages.” The narrative of that journey away from home to Europe or Canada or the United States and the later efforts to return may be said to be the Caribbean story, as suggested in the subtitle of Wilfred Cartey’s study of Caribbean literature, Whispers from the Caribbean: I Going Away, I Going Home, which argues that while in Caribbean literature there is much movement away, there is also a body of literature in which “the notion of ‘away’ and images of movement out are replaced by images of return” (xvi). Traditionally, however, the first autobiographical works, such as George Lamming’s In the Castle of My Skin, V. S. Naipaul’s A House for Mr. Biswas, Merle Hodge’s Crick Crack, Monkey, Jamaica Kincaid’s Annie John, Michelle Cliff’s No Telephone to Heaven, Edwidge Danticat’s Breath, Eyes, Memory, and Elizabeth Nunez’s Beyond the Limbo Silence, have focused on the childhood in the Caribbean and the journey away—or at least the preparation for that journey. Such is the case with Clarke’s “Leaving This Island Place.

Control parameterization for optimal control problems with continuous inequality constraints: New convergence results

Control parameterization is a powerful numerical technique for solving optimal control problems with general nonlinear constraints. The main idea of control parameterization is to discretize the control space by approximating the control by a piecewise-constant or piecewise-linear function, thereby yielding an approximate nonlinear programming problem. This approximate problem can then be solved using standard gradient-based optimization techniques. In this paper, we consider the control parameterization method for a class of optimal control problems in which the admissible controls are functions of bounded variation and the state and control are subject to continuous inequality constraints. We show that control parameterization generates a sequence of suboptimal controls whose costs converge to the true optimal cost. This result has previously only been proved for the case when the admissible controls are restricted to piecewise continuous functions

Optimal control of a delayed HIV model

We propose a model for the human immunodeficiency virus type 1 (HIV-1) infection with intracellular delay and prove the local asymptotical stability of the equilibrium points. Then we introduce a control function representing the efficiency of reverse transcriptase inhibitors and consider the pharmacological delay associated to the control. Finally, we propose and analyze an optimal control problem with state and control delays. Through numerical simulations, extremal solutions are proposed for minimization of the virus concentration and treatment costs.publishe

The control parameterization method for nonlinear optimal control: A survey

The control parameterization method is a popular numerical technique for solving optimal control problems. The main idea of control parameterization is to discretize the control space by approximating the control function by a linear combination of basis functions. Under this approximation scheme, the optimal control problem is reduced to an approximate nonlinear optimization problem with a finite number of decision variables. This approximate problem can then be solved using nonlinear programming techniques. The aim of this paper is to introduce the fundamentals of the control parameterization method and survey its various applications to non-standard optimal control problems. Topics discussed include gradient computation, numerical convergence, variable switching times, and methods for handling state constraints. We conclude the paper with some suggestions for future research

Higher Order Real-Time Approximations In Optimal Control Of Multibody-Systems For Industrial Robots

The multibody system of an industrial robot leads to a mathematical modell described by ordinary differential equations. Control functions have to be determined such that a given performance index is optimized subject to additional constraints. In order to solve such optimal control problems time-consuming methods are used which have no real-time capability. Hence a robust numerical method based on the parametric sensitivity analysis of nonlinear optimization problems is suggested. Real-time control approximations of perturbed optimal solutions can be obtained by evaluating a first order Taylor expansion of the perturbed solution. Successive improvement of the constraints in direction of the optimal perturbed solution leads to an admissible solution with a higher order approximation of the objective. The proposed numerical method is illustrated by the optimal control of an industrial robot subject to deviations in the payload and initial values

Differentiability of Consistency Functions for DAE Systems

In the present paper, parametric initial-value problems for differential-algebraic (DAE) systems are investigated. It is known that initial values of DAE systems must satisfy not only the original equations in the system but also derivatives of these equations with respect to time. Whether or not this actually imposes additional constraints on the initial values depends on the particular problem. Often the initial values are not determined uniquely, so that the resulting degrees of freedom can be used to optimize a given performance index. For this purpose, a class of functions is defined which will be called consistency functions. These functions map a set of parameters, which also include those undetermined initial values, to consistent initial values for the DAE system. Because of frequent gradient evaluations of the performance index and the constraints with respect to these system parameters needed by many optimization procedures, we state conditions such that the consistency functions represent differentiable functions with respect to these parameters

Lattice Boltzmann Constraints for Standard Optimization Problems

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