In this paper, we propose a computationally efficient algorithm for solving mixed-integer sampled optimization problems involving a large number of constraints. The proposed algorithm has a sequential nature. Specific...
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In this paper, we propose a computationally efficient algorithm for solving mixed-integer sampled optimization problems involving a large number of constraints. The proposed algorithm has a sequential nature. Specifically, at each iteration of the algorithm, the feasibility of a candidate solution is verified for all the constraints involved in the sampled optimization problem and violating constraints are identified. As a second step, an optimization problem is formed whose constraint set involves the current basis the minimal set of constraints defining the current candidate solution and a limited number of the observed violating constraints. We prove that the algorithm converges to the optimal solution in finite time. Additionally, we establish the effectiveness of the proposed algorithm using mixed-integer linear, and quadratically constrained quadratic programming problems. Copyright (C) 2020 The Authors.
We suggest a sequential algorithm for the detection of the ventricular fibrillation (VF) and ventricular tachycardia (VT) of a rate above 180 bpm, so called shockable rhythms. The built-in algorithm for ECG analysis e...
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ISBN:
(纸本)9781424441198
We suggest a sequential algorithm for the detection of the ventricular fibrillation (VF) and ventricular tachycardia (VT) of a rate above 180 bpm, so called shockable rhythms. The built-in algorithm for ECG analysis embedded in the portable bio-signal sensing module is aimed to discriminate between shockable and non-shockable rhythms and its accuracy is analyzed. An algorithm for VF/VT detection is proposed to analyze every 1 s ECG episode using the past 8 s episodes. The method is tested with 844,587 ECG episodes from the widely accepted databases. A sensitivity of 86.8 % and a specificity of 99.4 % were obtained and compared with the previous results.
In practical applications, there may exist a disparity between real values and optimal results due to uncertainties. This kind of disparity may cause violations of some probabilistic constraints in a reliability based...
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In practical applications, there may exist a disparity between real values and optimal results due to uncertainties. This kind of disparity may cause violations of some probabilistic constraints in a reliability based design optimization (RBDO) problem. It is important to ensure that the probabilistic constraints at the optimum in a RBDO problem are insensitive to the variations of design variables. In this paper, we propose a novel concept and procedure for reliability based robust design in the context of random uncertainty and epistemic uncertainty. The epistemic uncertainty of design variables is first described by an info gap model, and then the reliability-based robust design optimization (RBRDO) is formulated. To reduce the computational burden in solving RBRDO problems, a sequential algorithm using shifting factors is developed. The algorithm consists of a sequence of cycles and each cycle contains a deterministic optimization followed by an inverse robustness and reliability evaluation. The optimal result based on the proposed model satisfies certain reliability requirement and has the feasible robustness to the epistemic uncertainty of design variables. Two examples are presented to demonstrate the feasibility and efficiency of the proposed method. [DOI: 10.1115/1.4005442]
In this paper, we propose a computationally efficient algorithm for solving mixed-integer sampled optimization problems involving a large number of constraints. The proposed algorithm has a sequential nature. Specific...
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In this paper, we propose a computationally efficient algorithm for solving mixed-integer sampled optimization problems involving a large number of constraints. The proposed algorithm has a sequential nature. Specifically, at each iteration of the algorithm, the feasibility of a candidate solution is verified for all the constraints involved in the sampled optimization problem and violating constraints are identified. As a second step, an optimization problem is formed whose constraint set involves the current basis—the minimal set of constraints defining the current candidate solution—and a limited number of the observed violating constraints. We prove that the algorithm converges to the optimal solution in finite time. Additionally, we establish the effectiveness of the proposed algorithm using mixed-integer linear, and quadratically constrained quadratic programming problems.
A simple sequential algorithm was developed for constrained dynamic optimization of certain model types of fed-batch bioprocesses. The biotechnological and numerical basis as well as the application of the algorithm i...
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A simple sequential algorithm was developed for constrained dynamic optimization of certain model types of fed-batch bioprocesses. The biotechnological and numerical basis as well as the application of the algorithm is presented.
In this note we describe a sequential algorithm for the computation with specified accuracy on a segment of the largest value of a twice-differentiable function. The algorithm described does not require the computatio...
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In this note we describe a sequential algorithm for the computation with specified accuracy on a segment of the largest value of a twice-differentiable function. The algorithm described does not require the computation of the values of the derivative.
This study considers the problem of waveform design for colocated multiple-input multiple-output (MIMO) radars for multiple targets in the presence of multiple interferences in white Gaussian noise. Here, the authors ...
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This study considers the problem of waveform design for colocated multiple-input multiple-output (MIMO) radars for multiple targets in the presence of multiple interferences in white Gaussian noise. Here, the authors jointly design the transmit waveform and receive beamforming by a sequential algorithm. The proposed sequential algorithm maximises the minimum signal-to-interference-plus-noise ratio (SINR) to design both continuous and finite alphabet phase waveforms. In the case of continuous phase, all phases can be chosen in the waveform space, while in finite alphabet case, phases are only chosen from a confine set. Two important practical constraints of constant envelope' and similarity' are considered as well. The authors also have converted the waveform design problem into a quasi-convex optimisation problem which can be effectively solved by using convex optimisation toolbox (CVX). They have evaluated the performance of the matched filter output, beampattern and peak-to-average power ratio via numerical simulations and shown that the proposed sequential method achieves better SINR performance compared with existing MIMO radar transmit waveform design methods, for both single and multiple target scenarios.
Mixed-integer optimisation problems can be computationally challenging. Here, we introduce and analyse two efficient algorithms with a specific sequential design that are aimed at dealing with sampled problems within ...
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Mixed-integer optimisation problems can be computationally challenging. Here, we introduce and analyse two efficient algorithms with a specific sequential design that are aimed at dealing with sampled problems within this class. At each iteration step of both algorithms, we first test the feasibility of a given test solution for each and every constraint associated with the sampled optimisation at hand, while also identifying those constraints that are violated. Subsequently, an optimisation problem is constructed with a constraint set consisting of the current basis- namely, the smallest set of constraints that fully specifies the current test solution-as well as constraints related to a limited number of the identified violating samples. We show that both algorithms exhibit finite-time convergence towards the optimal solution. algorithm 2 features a neural network classifier that notably improves the computational performance compared to algorithm 1. We quantitatively establish these algorithms' efficacy through three numerical tests: robust optimal power flow, robust unit commitment, and robust random mixed-integer linear program.
In this paper a numerical approach combining the least squares method and the genetic algorithm (sequential and multi-core parallelization approach) is proposed for the determination of temperature in an inverse heat ...
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In this paper a numerical approach combining the least squares method and the genetic algorithm (sequential and multi-core parallelization approach) is proposed for the determination of temperature in an inverse heat conduction problem (IHCP). Some numerical experiments confirm the utility of this algorithm as the results are in good agreement with the exact data. Results show that an excellent estimation can be obtained by implementation sequential genetic algorithm within a CPU with clock speed 2.7 GHz, and parallel genetic algorithm within a 16-core CPU with clock speed 2.7 GHz for each core. (C) 2013 Elsevier Inc. All rights reserved.
This paper presents an automatic impedancematching algorithm that can sequentially realize a simultaneous conjugate-matching state for all the elements in an N-element array antenna. The analytical results show that t...
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ISBN:
(纸本)9781479948154
This paper presents an automatic impedancematching algorithm that can sequentially realize a simultaneous conjugate-matching state for all the elements in an N-element array antenna. The analytical results show that the converged solution obtained from the proposed algorithm agrees well with the solution calculated by the analytical deterministic equation, confirming the validity of the proposed method. The method can be applied to MRC and MIMO array antennas.
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