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IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Sun, Cong Zappone, Alessio Jorswieck, Eduard A. Beijing Univ Posts & Telecommun Sch Sci Beijing Peoples R China Univ Casino & Southern Lazio Casino Italy Tech Univ Dresden Commun Lab Commun Theory Dresden Germany

ISBN: (纸本)9781509041176

We consider the global energy efficiency (GEE) maximization problem for the general non-regenerative MIMO relay network, under the maximum power constraints for each user and each relay. The problem is reformulated through the fractional optimization technique, and the mean square error receiver filter is considered. By applying the alternating minimization method, we simplify the problem into several convex quadratic constrained quadratic pro gramming subproblems, and solve the subproblems by the feasible shrinkage method combined with the sequential quadratic programming method. Because the result highly depends on the initialization, we design a deterministic initialization by introducing an auxiliary power minimization problem. Simulation results show that our proposed algorithm can achieve more than 10 times higher GEE than the previous works which are not tailored for GEE maximization.

关键词： MIMO relay energy efficiency deterministic initialization sequential quadratic programming

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Comparison of Algorithms for High-Order, Metric-Based Mesh Optimization

Comparison of Algorithms for High-Order, Metric-Based Mesh O...

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AIAA Science and Technology Forum and Exposition (AIAA SciTech)

作者： Sanjaya, Devina P. Fidkowski, Krzysztof J. Murman, Scott M. Univ Tennessee Dept Mech Biomed & Aerosp Engn Knoxville TN 37996 USA Univ Michigan Dept Aerosp Engn Ann Arbor MI 48109 USA NASA Ames Res Ctr Moffett Field CA 94035 USA

ISBN: (数字)9781624105951

ISBN: (纸本)9781624105951

We assess the performance of two iterative mesh optimization algorithms for generating high-order, metric-conforming meshes appropriate for use with high-order, finite-element methods. The three key steps in these metric-based approaches are: 1) obtain the desired high-order, Riemannian metric field, 2) generate a linear, metric-conforming mesh, and 3) place the high-order geometry nodes such that the mesh-implied metric conforms to the desired metric field. The procedure to obtain the desired high-order metric field is similar to the Mesh Optimization via Error Sampling and Synthesis (MOESS) developed by Yano(1) for linear meshes. In this work, we consider two approaches to obtain the desired, higher-order metric field: 1) performing MOESS on a refined mesh, and 2) extending native MOESS to high-order MOESS (HOMES). The mesh regeneration procedures used to obtain the metric-conforming property are also described in detail. The accuracy benefits offered by these two proposed algorithms are presented for a series of one-dimensional problems. A two-dimensional proof of concept is also provided to establish the value of extending the proposed metric-based optimization algorithm to higher dimensions and to demonstrate the feasibility of the proposed mesh regeneration procedure in higher dimensions.

关键词： sequential quadratic programming Extended Finite Element Method Computational Fluid Dynamics Mesh Generation Peclet Number Computer Aided Design Discontinuous Galerkin Method Angle of Attack Shear Layers High Performance Computing Systems

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Combining flux and energy balance analysis to model large-scale biochemical networks

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Journal of Bioinformatics and Computational Biology 2006年第6期4卷 1227-1243页

作者： Heuett, William J. Qian, Hong Department of Physics University of Colorado Boulder CO 80309-0390 United States Department of Applied Mathematics University of Washington Seattle WA 98195-2420 Box 352420 United States

Stoichiometric Network Theory is a constraints-based, optimization approach for quantitative analysis of the phenotypes of large-scale biochemical networks that avoids the use of detailed kinetics. This approach uses the reaction stoichiometric matrix in conjunction with constraints provided by flux balance and energy balance to guarantee mass conserved and thermodynamically allowable predictions. However, the flux and energy balance constraints have not been effectively applied simultaneously on the genome scale because optimization under the combined constraints is non-linear. In this paper, a sequential quadratic programming algorithm that solves the non-linear optimization problem is introduced. A simple example and the system of fermentation in Saccharomyces cerevisiae are used to illustrate the new method. The algorithm allows the use of non-linear objective functions. As a result, we suggest a novel optimization with respect to the heat dissipation rate of a system. We also emphasize the importance of incorporating interactions between a model network and its surroundings. © Imperial College Press.

关键词： Constraints-based optimization Reaction networks sequential quadratic programming Stoichiometric Network Theory Systems biology Yeast glycolysis

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Structure Characteristic Analysis and Optimization Design for Microsatellite

Structure Characteristic Analysis and Optimization Design fo...

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10th International Conference on Machining and Advanced Manufacturing Technology

作者： Zheng, Kan Liao, Wenhe Zhang, Xiang Nanjing Univ Aeronaut & Astronaut Coll Mech & Elect Engn Nanjing Peoples R China Nanjing Univ Aeronaut & Astronaut Acad Frontier Sci Nanjing Peoples R China

ISBN: (纸本)0878492844

According to the structural layout and mechanics characteristic of microsatellite, the FEM was established reasonably. Base on the FEM analysis and its characteristics, the structure of microsatellite was optimization designed. In the optimization process, the optimization model was established with the design variables of aluminum panel thickness, core plate thickness and skeleton thickness, and subjected to stiffness, strength, displacement and size constraints. Then, used the sequential quadratic programming method for optimization analysis. The results of the optimization demonstrates that the weight of structure loss significantly, and the whole structure weight of the microsatellite loss 11%.Meanwhile, the iterative times of the optimization process is few, so it is very Meaningful and useful for actual project application.

关键词： Microsatellite FEM Optimization design sequential quadratic programming Honeycomb sandwich panel

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Large-scale multidisciplinary design optimization: review and recommendations

Large-scale multidisciplinary design optimization: review an...

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AIAA Aviation Forum and Exposition

作者： Hwang, John T. Ha, Tae H. Jain, Arnav, V Univ Calif San Diego Dept Mech & Aerosp Engn La Jolla CA 92093 USA

ISBN: (数字)9781624105890

ISBN: (纸本)9781624105890

Large-scale multidisciplinary design optimization is the solution of engineering design problems using numerical optimization involving large numbers of design variables and disciplines. Efficient and exact derivative computation is necessary for scalability, but it is time-consuming to implement, especially with complex, multidisciplinary models. The recent emergence of a modular framework-based approach to assist in multidisciplinary derivative computation has led to a rapid increase in methods and applications for large-scale multidisciplinary design optimization. This paper provides an overview of the state-of-the-art approach, surveys recent applications, and provides recommendations for future applications. The recommendations are based on experience from prior applications as well as benchmarking studies involving satellite system design, eVTOL air taxi system design, the vortex lattice method, finite element analysis, and an optimal control problem. In particular, the recommendations deal with the choice of MDO architecture, optimization algorithm, the impact of exact versus approximate derivative computation, constraint aggregation, and optimal control.

关键词： Multidisciplinary Design Optimization Trajectory Optimization sequential quadratic programming Numerical Optimization Satellites Computational Fluid Dynamics Finite Element Analysis Thrust Specific Fuel Consumption Finite Difference Approximation Blade Element Momentum Theory

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Optimal Notch Filter for Active Magnetic Bearing Controllers

Optimal Notch Filter for Active Magnetic Bearing Controllers

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IEEE/ASME International Conference on Advanced Intelligent Mechatronics

作者： Yoo, Seong-yeol Lee, Wook-ryun Bae, Yong-chae Noh, Myounggyu D. Chungnam Natl Univ Dept Mechatron Engn Taejon South Korea Korea Elect Power Res Inst Taejon South Korea

ISBN: (纸本)9781457708398

This paper describes an optimal design of a notch filter for the active magnetic bearing (AMB) system which has unstable poles. A Notch filter is designed as 2(nd) order standard transfer function with two zeros and two poles. We performed optimization to design a notch filter using sequential quadratic programming (SQP) and evaluate the closed loop stability with a designed notch filter. The designed notch filter is optimized to have the lowest peak sensitivity with respect to the disturbance closed loop system. The designed notch filter is examined for the compensation effect for the unstable 2(nd) order system. We applied the design optimization to large scale flywheel energy storage system (FESS). FESS can store up to 5kWh of usable energy at the maximum speed of 18,000 rpm. The effect of the designed filter from optimization is simulated using simulation model of FESS and the simulation model is validated against experiments.

关键词： FESS Flywheels Magnetic levitation Magnetic separation Optimization Rotors SQP Sensitivity Transfer functions active magnetic bearing controllers closed loop stability closed loop systems disturbance closed loop system flywheels large scale flywheel energy storage system magnetic bearings notch filters optimal notch filter poles and zeros poles and zeros quadratic programming sequential quadratic programming stability transfer function

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Nonlinear Constrained Optimization in R and Its Application for Sufficient Dimension Reduction and Variable Selection

Nonlinear Constrained Optimization in R and Its Application ...

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作者： Xianyan Chen The University of Georgia

学位级别：博士

In this dissertation, we develop an R package, NlcOptim, to solve the optimization problems with nonlinear objective functions and nonlinear constraints. This pack- age can be used to solve problems in sufficient dimension reduction and variable selection because of its capability to accept the input parameters as a constrained matrix. We propose a framework for dimension reduction problems via Distance Co- variance (DCOV) where both the response and the predictor are vectors. In this framework, distance covariance method is employed to estimate the central subspace effectively, and we also propose two different methods based on projective resampling technique to transfer multivariate response to univariate response. This approach keeps the model-free advantage, and can fully recover the central subspace even when many predictors are discrete. We then extend DCOV methods to canonical analy- sis, termed as Canonical Distance Covariance Analysis (CDCA), where we explore the relationships between two multivariate sets of variables. In addition, we extend DCOV to estimate the dual central subspace (DCS), which is to find the basis that span the subspace of Y Y Y as well as the basis that span the subspace of X X X. At last, we develop a new concept, termed the Dual Variable Selection (DVS), to propose a method for simultaneously selecting subsets for each of the two random vectors, by employing DCOV method combined with LASSO penalty.

关键词： Central subspace Distance Covariance Dual Central subspace Dual Variable Selection Nonlinear constraints Nonlinear optimization Projective Resampling sequential quadratic programming Sufficient Dimension Reduction

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Efficient Second-Order Methods for Second-Order Cone Programs and Continuous Nonlinear Two-Stage Optimization Problems

Efficient Second-Order Methods for Second-Order Cone Program...

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作者： Luo, Xinyi Northwestern University

学位级别：Ph.D., Doctor of Philosophy

This dissertation presents novel advancements in the field of continuous nonlinear optimization, focusing on the development of efficient second-order methods for second-order conic programs (SOCPs) and continuous nonlinear two-stage optimization problems. The primary focus is on the theory and computations of sequential quadratic programming (SQP) methods, which are widely used for solving nonlinear optimization problems. In this work, we introduce a new SQP method tailored for linear SOCPs, leveraging the SQP framework for nonlinear optimization. By capitalizing on warm-start capabilities for active-set quadratic programming subproblem solvers and utilizing polyhedral outer approximations of cones, our method achieves a local quadratic rate of convergence. It efficiently identifies the set of cones where the optimal solution lies at extreme points, enabling rapid local convergence. Numerical experiments confirm that the method can take advantage of good starting points and can achieve higher accuracy compared to a state-of-the-art interior point solver. The dissertation also includes two computational projects. Firstly, we introduce Restart-SQP, an open-source C++ software package implementing Fletcher’s Sℓ1QP method and integrating the parametric active-set methods for solving SQP subproblems. RestartSQP offers unique warm-start capabilities, efficiently handling changing constraint values and incorporating additional constraints dynamically. Additionally, it supports crossover from interior-point solvers like Ipopt, enabling the solution of subsequent NLPs using the SQP method. Numerical experiments using the CUTE benchmark NLP test set demonstrate the practical performance of RestartSQP. Furthermore, we develop a two-stage decomposition algorithm for continuous nonlinear two-stage optimization problems and discussed the detailed C++ implementation. The algorithm decomposes large-scale problems into master and independent subproblems, allowing for parallel proce

关键词： Nonlinear optimization sequential quadratic programming Two-stage optimization Optimization problems

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AN EFFICIENT NUMERICAL METHOD FOR THE SOLUTION OF THE L₂ OPTIMAL MASS TRANSFER PROBLEM

AN EFFICIENT NUMERICAL METHOD FOR THE SOLUTION OF THE <i>L</...

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10th Copper Mountain Conference on Iterative Methods

作者： Haber, Eldad Rehman, Tauseef Tannenbaum, Allen Emory Univ Dept Math & Comp Sci Atlanta GA 30322 USA Georgia Inst Technol Sch Elect & Comp Engn Atlanta GA 30332 USA Georgia Inst Technol Sch Elect & Comp Atlanta GA 30332 USA Georgia Inst Technol Sch Biomed Engn Atlanta GA 30332 USA

In this paper we present a new computationally efficient numerical scheme for the minimizing flow approach for the computation of the optimal L-2 mass transport mapping. In contrast to the integration of a time dependent partial differential equation proposed in [S. Angenent, S. Haker, and A. Tannenbaum, SIAM J. Math. Anal., 35 (2003), pp. 61-97], we employ in the present work a direct variational method. The efficacy of the approach is demonstrated on both real and synthetic data.

关键词： optimal mass transport sequential quadratic programming variational formulation

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The impact of unstructured mesh generation approach on truncation error 54

The impact of unstructured mesh generation approach on trunc...

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54th AIAA Aerospace Sciences Meeting, 2016

作者： Fan, Hongliang Ollivier-Gooch, Carl Department of Mechanical Engineering The University of British Columbia VancouverBCV6T 1Z4 Canada

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