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A REVISED sequential quadratic SEMIDEFINITE programming METHOD FOR NONLINEAR SEMIDEFINITE OPTIMIZATION

JOURNAL OF INDUSTRIAL AND MANAGEMENT OPTIMIZATION 2023年第10期19卷 7777-7794页

作者： Okabe, Kosuke Yamakawa, Yuya Fukuda, Ellen hidemi Kyoto Univ Grad Sch Informat Kyoto Japan

In 2020, Yamakawa and Okuno proposed a stabilized sequential quadratic semidefinite programming (SQSDP) method for solving, in particular, degenerate nonlinear semidefinite optimization problems. The algorithm is shown to converge globally without a constraint qualification, and it has some nice properties, including the feasible subproblems, and their possible inexact computations. In particular, the convergence was established for approximateKarush-Kuhn-Tucker (AKKT) and trace-AKKT conditions, which are two sequential optimality conditions for the nonlinear conic contexts. However, recently, complementarity-AKKT (CAKKT) conditions were also considered, as an alternative to the previous mentioned ones, that is more practical. Since few methods are shown to converge to CAKKT points, at least in conic optimization, and to complete the study associated to the SQSDP, here we propose a revised version of the method, maintaining the good properties. We modify the previous algorithm, prove the global convergence in the sense of CAKKT, and show some preliminary numerical experiments.

关键词： Approximate Karush-Kuhn-Tucker conditions sequential quadratic programming sequential optimality conditions nonlinear semidefinite programming

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Integrated computational intelligent paradigm for nonlinear electric circuit models using neural networks, genetic algorithms and sequential quadratic programming

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NEURAL COMPUTING & APPLICATIONS 2020年第14期32卷 10337-10357页

作者： Mehmood, Ammara Zameer, Aneela Ling, Sai Ho Rehman, Ata Ur Raja, Muhammad Asif Zahoor Pakistan Inst Engn & Appl Sci Dept Elect Engn Islamabad Pakistan Pakistan Inst Engn & Appl Sci Dept Comp & Informat Sci Islamabad Pakistan Univ Technol Sch Biomed Engn Dept Engn & IT Ctr Hlth Technol Sydney NSW Australia COMSATS Univ Islamabad Dept Elect & Comp Engn Attock Campus Attock Pakistan

In this paper, a novel application of biologically inspired computing paradigm is presented for solving initial value problem (IVP) of electric circuits based on nonlinear RL model by exploiting the competency of accurate modeling with feed forward artificial neural network (FF-ANN), global search efficacy of genetic algorithms (GA) and rapid local search with sequential quadratic programming (SQP). The fitness function for IVP of associated nonlinear RL circuit is developed by exploiting the approximation theory in mean squared error sense using an approximate FF-ANN model. Training of the networks is conducted by integrated computational heuristic based on GA-aided with SQP, i.e., GA-SQP. The designed methodology is evaluated to variants of nonlinear RL systems based on both AC and DC excitations for number of scenarios with different voltages, resistances and inductance parameters. The comparative studies of the proposed results with Adam's numerical solutions in terms of various performance measures verify the accuracy of the scheme. Results of statistics based on Monte-Carlo simulations validate the accuracy, convergence, stability and robustness of the designed scheme for solving problem in nonlinear circuit theory.

关键词： Artificial neural networks Nonlinear systems Nonlinear electric circuits Genetic algorithms sequential quadratic programming

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Rate and equilibrium based modeling with the sequential quadratic programming optimization method for glycol dehydration of produced natural gas

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BRAZILIAN JOURNAL OF CHEMICAL ENGINEERING 2020年第4期37卷 745-756页

作者： Okoro, E. E. Otuekpo, J. E. Ekeinde, E. B. Dosunmu, A. Covenant Univ Ota Petr Engn Dept Ota Nigeria Fed Minist Ind Trade & Investment Abuja Abuja Nigeria Univ Port Harcourt Petr Engn Dept Port Harcourt Nigeria

Determining the optimum temperature and pressure for the dehydration of natural gas in a glycol absorption unit and the recovery of the glycol from the glycol water mixture in a desorption unit is of great importance. Although, the equilibrium base model for the absorption column design had been in use, the rate based model for the absorption unit offers a promising technique and had been proven to be more accurate in determining the parameters for the design. In this study, dehydration of a natural gas plant was modelled with optimization of its parameters. The effects on cost were adequately studied. Sensitivity analysis resulting from the simulation showed that a lower temperature for effective absorption of the water from the gas stream by triethylene glycol (TEGlycol) solvent is expected, while a higher temperature and higher reboiler duty is required for the regeneration of the solvent from the Rich TEGlycol stream in a distillation column. The sequential quadratic programming (SQP) direct optimization method was employed to optimize the major parameters of the natural gas dehydration plant. The optimum temperature of 267 degrees F and Reboiler duty of 169,789 Btu/h gave a 0.99 TEGlycol recovery purity. A minimized capital cost of 3.73 million US Dollars and operating cost of approximately 1 million US dollars was also observed.

关键词： Natural gas dehydration sequential quadratic programming Optimum condition for dehydration Glycol Mathematical model Simulation

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A Fast Algorithm for Maximum Likelihood Estimation of Mixture Proportions Using sequential quadratic programming

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JOURNAL OF COMPUTATIONAL AND GRAPHICAL STATISTICS 2020年第2期29卷 261-273页

作者： Kim, Youngseok Carbonetto, Peter Stephens, Matthew Anitescu, Mihai Univ Chicago Dept Stat Chicago IL 60637 USA Univ Chicago Dept Human Genet Chicago IL 60637 USA Univ Chicago Res Comp Ctr Chicago IL 60637 USA Argonne Natl Lab Math & Comp Sci Div 9700 S Cass Ave Lemont IL 60439 USA

Maximum likelihood estimation of mixture proportions has a long history, and continues to play an important role in modern statistics, including in development of nonparametric empirical Bayes methods. Maximum likelihood of mixture proportions has traditionally been solved using the expectation maximization (EM) algorithm, but recent work by Koenker and Mizera shows that modern convex optimization techniques-in particular, interior point methods-are substantially faster and more accurate than EM. Here, we develop a new solution based on sequential quadratic programming (SQP). It is substantially faster than the interior point method, and just as accurate. Our approach combines several ideas: first, it solves a reformulation of the original problem;second, it uses an SQP approach to make the best use of the expensive gradient and Hessian computations;third, the SQP iterations are implemented using an active set method to exploit the sparse nature of the quadratic subproblems;fourth, it uses accurate low-rank approximations for more efficient gradient and Hessian computations. We illustrate the benefits of the SQP approach in experiments on synthetic datasets and a large genetic association dataset. In large datasets (n approximate to 106observations,m approximate to 103mixture components), our implementation achieves at least 100-fold reduction in runtime compared with a state-of-the-art interior point solver. Our methods are implemented in Julia and in an R package available on CRAN (). Supplementary materials for this article are available online.

关键词： Active set methods Convex optimization Mixture models Nonparametric empirical Bayes Rank-revealing QR decomposition sequential quadratic programming

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YAM2: Yet another library for the M2 variables using sequential quadratic programming

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COMPUTER PHYSICS COMMUNICATIONS 2021年 264卷 107967-107967页

作者： Park, Chan Beom Inst Basic Sci IBS Ctr Theoret Phys Universe 55 Expo Ro Daejeon 34126 South Korea

The M-2 variables are devised to extend M-T2 by promoting transverse masses to Lorentz-invariant ones and making explicit use of on-shell mass relations. Unlike simple kinematic variables such as the invariant mass of visible particles, where the variable definitions directly provide how to calculate them, the calculation of the M-2 variables is undertaken by employing numerical algorithms. Essentially, the calculation of M-2 corresponds to solving a constrained minimization problem in mathematical optimization, and various numerical methods exist for the task. We find that the sequential quadratic programming method performs very well for the calculation of M-2, and its numerical performance is even better than the method implemented in the existing software package for M-2. As a consequence of our study, we have developed and released yet another software library, YAM2, for calculating the M-2 variables using several numerical algorithms.

关键词： Collider physics Kinematic variable Constrained optimization sequential quadratic programming

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Path Planning for Unmanned Surface Vehicle based on genetic algorithm and sequential quadratic programming

Path Planning for Unmanned Surface Vehicle based on genetic ...

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Chinese Automation Congress (CAC)

作者： Zhuang, Yufei Wang, Cheng Huang, Haibin Harbin Inst Technol Sch Informat Sci & Engn Weihai Peoples R China Harbin Inst Technol Sch Astronaut Harbin Peoples R China

ISBN: (纸本)9781728176871

Path planning and obstacle avoidance of Unmanned Surface Vehicle (USV) is one of the hottest research topics in modern national defense and ocean engineering. Considering the issue of obstacle-free path planning of USV, this paper focuses on a 3-DoF USV and develops an algorithm design. We adopt Gauss pseudo-spectral method to discretize control model and make use of a hybrid algorithm to optimize which combines the advantage of genetic algorithm and sequential quadratic programming algorithm. Simulation results show that this method can quickly explore a high-precision route in an unknown environment which meets the mobility requirement of USV without setting the initial value artificially.

关键词： Unnumned Surface Vehicle ((IS t) path planning genetic algorithm sequential quadratic programming

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Co-design of a Continuously Variable Transmission using sequential quadratic programming-based Control Optimization 21st

Co-design of a Continuously Variable Transmission using Sequ...

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21st IFAC World Congress on Automatic Control - Meeting Societal Challenges

作者： Fahdzyana, C. A. Donkers, M. C. F. Hofman, T. Eindhoven Univ Technol Eindhoven Netherlands

For systems with nonlinear dynamics, Dynamic programming for control is commonly considered in the framework of integrated plant and control system design. Despite its popularity, this control strategy can run into some computational issues as the performance is dependent on the state and input discretization. In this paper, we propose a sequential quadratic programming-based control optimization strategy for integrated system design, where both the plant and control are optimized for the case study of a continuously variable transmission. The proposed plant and control design problem will be solved using a nested strategy. Copyright (C) 2020 The Authors.

关键词： Multi-objective optimization optimization optimal control co-design sequential quadratic programming

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sequential quadratic programming enhanced backtracking search algorithm

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Frontiers of Computer Science 2018年第2期12卷 316-330页

作者： Wenting ZHAO Lijin WANG Yilong YIN Bingqing WANG Yuchun TANG School of Computer Science and Technology Shandong University Jinan 250101 China College of Computer and Information Science Fujian Agriculture and Forestry University Fuzhou 350002 China Research Center for Sectional and Imaging Anatomy Shandong University School of Medicine Jinan 250012 China

In this paper, we propose a new hybrid method called SQPBSA which combines backtracking search optimization algorithm （BSA） and sequential quadratic programming （SQP）. BSA, as an exploration search engine, gives a good direction to the global optimal region, while SQP is used as a local search technique to exploit the optimal solution. The experiments are carried on two suits of 28 functions proposed in the CEC-2013 competitions to verify the performance of SQPBSA. The results indicate the proposed method is effective and competitive.

关键词： numerical optimization backtracking search algorithm sequential quadratic programming local search

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Varying-Radius Tunnel-Following NMPC for Robot Manipulators Using sequential Convex quadratic programming 2nd

Varying-Radius Tunnel-Following NMPC for Robot Manipulators ...

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2nd Modeling, Estimation and Control Conference (MECC)

作者： Astudillo, Alejandro Pipeleers, Goele Gillis, Joris Decre, Wilm Swevers, Jan Katholieke Univ Leuven MECO Res Team Dept Mech Engn DMMS Lab B-3001 Leuven Belgium

The tunnel-following nonlinear model predictive control (NMPC) scheme for robot manipulators allows the definition of tasks where deviations from a given path reference are allowed but upper-bounded by a user-defined parameter, which for a position tunnel represents the radius of the tunnel. The underlying optimal control problem (OCP) in this scheme can be efficiently solved by using the sequential convex quadratic programming (SCQP) method. Up to now, this scheme has been implemented with constant tunnel radii, although several tasks, such as human-robot collaboration or pick-and-place tasks, would benefit from variable radii throughout task execution. The SCQP method is however not able to exploit the structure of varying-radius tunnel constraints, which can lead to unstable iterations of the SQP method. In this work, we propose a reformulation of the tunnel constraints to overcome this issue, allowing the use of the SCQP method to efficiently solve the underlying OCP. We also provide insight into an efficient implementation of the SCQP method using the lin operator and prove the main theorem underlying such operator. Simulation results of a task involving a varying-radius tunnel are presented to support the applicability of the proposed methods. Copyright (c) 2022 The Authors. This is an open access article under the CC BY-NC-ND license (https://***/licenses/by-nc-nd/4.0)

关键词： Optimal control robot manipulators model predictive control tunnel following sequential quadratic programming

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A multiplicative regularized Gauss-Newton method with trust region sequential quadratic programming for structural model updating

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MECHANICAL SYSTEMS AND SIGNAL PROCESSING 2019年 131卷 417-433页

作者： Mazzotti, Matteo Mao, Qiang Bartoli, Ivan Livadiotis, Stylianos Drexel Univ Civil Architectural & Environm Engn Dept 3141 Chestnut St Philadelphia PA 19104 USA

The paper focuses on the development of an iterative minimization algorithm for structural identification. The algorithm consists of a Gauss-Newton method in which the ill-conditioning caused by noise pollution is mitigated by means of a multiplicative regularization technique used in conjunction with a bound constrained trust region method. Unlike the classic additive regularization technique, the amount of regularization is not determined a priori, but computed in an automatic fashion at each step of the iterative procedure. Specifically, the strength of the regularization is controlled by the norm of the model parameters weighted by a factor proportional to the current values of the least-square cost functional and the size of the trust region. The iterative procedure consists in solving a sequence of regularized local quadratic subproblems in a sequential quadratic programming framework, for which a local convexity condition is given. The proposed method is finally tested in the retrieval of the equivalent stiffness of the soil and bearings of a real, in-service bridge pier that was tested using experimental modal analysis. (C) 2019 Elsevier Ltd. All rights reserved.

关键词： Model updating Inverse problem Multiplicative regularization sequential quadratic programming Modal analysis

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