This paper proposes a frequency domain decision feedback data receiver for the uplink transmission of broadband single carrier cyclic prefix-assisted CDMA systems. The optimum data detection problem based on the maxim...
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This paper proposes a frequency domain decision feedback data receiver for the uplink transmission of broadband single carrier cyclic prefix-assisted CDMA systems. The optimum data detection problem based on the maximal-likelihood criteria for this system is addressed as a combinatorial optimization problem. A sequential quadratic programming approach is proposed to solve this problem. The parallel gradient projection method and projected successive over relaxation algorithm are proposed to solve the sequential quadratic programming problem, which corresponds to low-complexity nonlinear parallel and successive interference cancellation schemes in frequency domain. The convergence properties and the complexities of these methods are analyzed and compared with conventional methods. The simulation results show that, with a few iterations, the proposed scheme gives near single user performance even for fully loaded systems. (c) 2008 Elsevier Inc. All rights reserved.
Efficient sequential quadratic programming (SQP) implementations are presented for equality-constrained, discrete-time, optimal control problems. The algorithm developed calculates the search direction for the equalit...
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Efficient sequential quadratic programming (SQP) implementations are presented for equality-constrained, discrete-time, optimal control problems. The algorithm developed calculates the search direction for the equality-based variant of SQP and is applicable to problems with either fixed or free final time. Problem solutions are obtained by solving iteratively a series of constrained quadratic programs. The number of mathematical operations required for each iteration is proportional to the number of discrete times N. This is contrasted by conventional methods in which this number is proportional to N-3. The algorithm results in quadratic convergence of the iterates under the same conditions as those for SQP and simplifies to an existing dynamic programming approach when there are no constraints and the final time is fixed. A simple test problem and two application problems are presented. The application examples include a satellite dynamics problem and a set of brachistochrone problems involving viscous friction.
In this paper, mixed integer nonlinear programming (MINLP) is optimized by PSO_GA-SQP, the mixed coding of a particle swarm optimization (PSO), and a hybrid genetic algorithm and sequential quadratic programming (GA-S...
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In this paper, mixed integer nonlinear programming (MINLP) is optimized by PSO_GA-SQP, the mixed coding of a particle swarm optimization (PSO), and a hybrid genetic algorithm and sequential quadratic programming (GA-SQP). The population is separated into two groups: discrete and continuous variables. The discrete variables are optimized by the adapted PSO, while the continuous variables are optimized by the GA-SQP using the discrete variable information from the adapted PSO. Therefore, the population can be set to a smaller size than usual to obtain a global solution. The proposed PSO_GA-SQP algorithm is verified using various MINLP problems including the designing of retrofit heat exchanger networks. The fitness values of the tested problems are able to reach the global optimum.
Described here is the structure and theory for a sequential quadratic programming algorithm for solving sparse nonlinear optimization problems. Also provided are the details of a computer implementation of the algorit...
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Described here is the structure and theory for a sequential quadratic programming algorithm for solving sparse nonlinear optimization problems. Also provided are the details of a computer implementation of the algorithm along with test results. The algorithm maintains a sparse approximation to the Cholesky factor of the Hessian of the Lagrangian. The solution to the quadratic program generated at each step is obtained by solving a dual quadratic program using a projected conjugate gradient algorithm. An updating procedure is employed that does not destroy sparsity.
This paper presents a new hybrid algorithm of Harris hawks' optimization with sequential quadratic programming (HHO-SQP) for optimal coordination of directional overcurrent relays to find optimal relays settings. ...
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This paper presents a new hybrid algorithm of Harris hawks' optimization with sequential quadratic programming (HHO-SQP) for optimal coordination of directional overcurrent relays to find optimal relays settings. The SQP procedure is employed in the hybrid algorism as a local search mechanism to enhance the performance of the original HHO method. The optimization problem is described based on a developed objective function as a non-linear and highly constrained optimization problem to minimize the total operating time for primary relays at the same time of maximizing the backup relays operating time. The developed objective function is subject to some constraints related to the coordination process including the absence of any miss coordination between primary and backup relays. The performance of the proposed algorithm based on the new objective function is implemented for two different test systems. The results of the proposed algorithm is compared with those obtained from other recent meta-heuristic techniques. The results show that the new hybrid algorithm outperforms the recently published meta-heuristic algorithms. (C) 2020 THE AUTHORS. Published by Elsevier BV on behalf of Faculty of Engineering, Alexandria University.
Robust design with dynamic characteristics is an important oft-line quality engineering technique for improving product quality over a range of input conditions by reducing variations caused by uncontrolled factors. S...
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Robust design with dynamic characteristics is an important oft-line quality engineering technique for improving product quality over a range of input conditions by reducing variations caused by uncontrolled factors. Since several studies have indicated that there are important limitations to Taguchi's S/N ratio analysis, the solution procedure for dynamic systems deserves further investigation. This paper proposes a stochastic optimization modeling procedure to overcome the difficulty in Taguchi's method to accommodate dynamic characteristics. The main idea underlying the proposed method is to minimize the total variations on quality characteristics while attaining the target performance over a range of input conditions. Due to the nonlinear nature of the stochastic optimization model, two stochastic versions of sequential quadratic programming respectively embedded with a Monte Carlo simulation and numerical approximations are devised to solve the problem. In the robust design of a temperature control circuit often discussed in dynamic problems, the proposed method performs efficiently and effectively. Compared with the Taguchi method, the design solved in this paper has smaller variations, indicating that the proposed method is a promising technique for dynamic-characteristic robust design.
We consider optimization problems with objective and constraint functions that may be nonconvex and nonsmooth. Problems of this type arise in important applications, many having solutions at points of nondifferentiabi...
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We consider optimization problems with objective and constraint functions that may be nonconvex and nonsmooth. Problems of this type arise in important applications, many having solutions at points of nondifferentiability of the problem functions. We present a line search algorithm for situations when the objective and constraint functions are locally Lipschitz and continuously differentiable on open dense subsets of R-n. Our method is based on a sequential quadratic programming (SQP) algorithm that uses an l(1) penalty to regularize the constraints. A process of gradient sampling (GS) is employed to make the search direction computation effective in nonsmooth regions. We prove that our SQP-GS method is globally convergent to stationary points with probability one and illustrate its performance with a MATLAB implementation.
United multi-operator evolutionary algorithms (UMOEAs) combine multi-operator differ-ential evolution (DE), the multi-operator genetic algorithm (MOGA), and the covariance matrix adaption evolution strategy (CMA-ES). ...
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United multi-operator evolutionary algorithms (UMOEAs) combine multi-operator differ-ential evolution (DE), the multi-operator genetic algorithm (MOGA), and the covariance matrix adaption evolution strategy (CMA-ES). UMOEAs-II, an improved version of UMOEAs, uses three differential evolution variants as multi-operator differential evolution (MODE), and CMA-ES. In this study, we further reform UMOEAs-II using an improved SHADE-cnEpSin that employs a novel adaptive strategy of scaling factor F, a crossover rate cri;j updating mechanism which can calculate crossover rate for the ith individual with a particular jth component, an improved rank-based selective pressure based mutation strat-egy, and nonlinear population size reduction along with sequential quadratic programming method. The effectiveness of the improved rank-based selective pressure based mutation strategy, nonlinear population size reduction, and sequential quadratic programming are evident from the individual validations. The novel framework, enhanced the exploration and exploitation abilities, is named UMOEAs-III and is evaluated using the CEC2017 bench-mark functions. The experiments are tested on 10, 30, 50, and 100 dimensions. The exper-imental results demonstrate the outstanding performance of UMOEAs-III in both low and high-dimensional tests compared to the state-of-the-art DE-based variants and hybrid algorithms.(c) 2022 Elsevier Inc. All rights reserved.
We consider sequential quadratic programming methods for solving constrained nonlinear programming problems. It is generally believed that these methods are sensitive to the accuracy by which partial derivatives are p...
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We consider sequential quadratic programming methods for solving constrained nonlinear programming problems. It is generally believed that these methods are sensitive to the accuracy by which partial derivatives are provided. One reason is that differences of gradients of the Lagrangian function are used for updating a quasi-Newton matrix, e.g., by the BFGS formula. The purpose of this paper is to show by numerical experimentation that the method can be stabilized substantially. The algorithm applies non-monotone line search and internal and external restarts in case of errors due to inaccurate derivatives while computing the search direction. Even in case of large random errors leading to partial derivatives with at most one correct digit, termination subject to an accuracy of 10(-7) can be achieved in 90% of 306 problems of a standard test suite. On the other hand, the original version with monotone line search and without restarts solves only 30% of these problems under the same test environment. In addition, we show how initial and periodic scaled restarts improve the efficiency in situations with slow convergence.
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