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Targeted reproduction of interior sound field for mitigating negative acoustics effects based on nonlinear programming

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APPLIED ACOUSTICS 2025年 236卷

作者： Li, Xu Wu, Xudong Huang, Jin Feng, Qitao Tongji Univ Sch Automot Studies Shanghai 201804 Peoples R China Shanghai Key Lab Vehicle Aerodynam & Vehicle Therm Shanghai 201804 Peoples R China

To address the interior negative acoustic effects (NAEs) generated in vehicular sound cavities during acoustic modulation, this investigation proposes a targeted reproduction approach oriented towards the interior sound field based on nonlinear programming. For a given vehicular sound cavity, the sound source locations matching the acoustical transmission responses in vehicle environment is determined, and NAEs composed of scattering and reflection effects are interpreted and calculated. By the numerical equivalence of three reproductive challenges, the targeted sound field reproduction (SFR) based on nonlinear programming is formulated. Combined with the practical loudspeaker properties, the influence of NAEs on reproduced sound fields and its mitigation are analyzed. An experimental verification platform corresponding to vehicular sound cavity is constructed, so the different performances with the free fields and vehicle driving conditions are investigated, respectively. Simulation and measurement results both demonstrate that the proposed method not only achieves brilliant SFR performance, but also effectively achieves NAEs mitigation through targeted compensation mechanism. This research, starting from the acoustical characteristic of vehicular sound cavity and the equivalence calculation for interior NAEs, can provide technical support for high-quality acoustical performances in vehicle environment.

关键词： Acoustic modulation Sound field reproduction nonlinear programming Negative acoustic effects Scattering effects

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Integrated optimization of humanitarian relief chains: a multi-product nonlinear programming approach for disaster logistics under uncertainty

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INTERNATIONAL JOURNAL OF SYSTEM ASSURANCE ENGINEERING AND MANAGEMENT 2025年第2期16卷 425-445页

作者： Singh, Jitendra Jaggi, Chandra K. Univ Delhi Fac Math Sci Dept Operat Res New Delhi 110007 India

This research introduces a novel approach to optimize the distribution of relief items in post-disaster scenarios. Unlike traditional approaches, the model integrates dynamic demand and supply considerations, accounting for fluctuations in population movement within relief camps. The model uniquely incorporates the impact of disaster severity and risk on transportation costs, enabling a more realistic assessment of logistical challenges. By minimizing total costs, including transportation, inventory, and disposal of damaged items, our approach ensures cost-efficient distribution without compromising service delivery levels. A numerical example with a post-disaster relief distribution scenario demonstrates the model's practical application. Sensitivity analysis with multiple parameters confirms the model's robustness and potential to guide efficient and effective humanitarian relief operations. This study contributes to the field of relief chain optimization by offering a comprehensive framework that accounts for several factors influencing disaster logistics. The research highlights the model's potential as a valuable tool for improving the efficiency and effectiveness of humanitarian relief efforts.

关键词： Relief chain Inventory control Distribution network nonlinear programming Humanitarian operation

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Using regression trees for improving the efficiency of nonlinear programming solvers

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ENGINEERING SCIENCE AND TECHNOLOGY-AN INTERNATIONAL JOURNAL-JESTECH 2025年 64卷

作者： Terzi, Fulya Aras, Necati Baydogan, M. Gokce Bogazici Univ Dept Ind Engn TR-34342 Istanbul Turkiye

Optimization plays a critical role in fields such as economics, engineering, and computational sciences, where finding the optimal values of decision variables is essential for the design of a product, production system, or service system. However, many optimization problems remain challenging even with advanced solvers. This study integrates machine learning into optimization by employing a regression tree algorithm that is trained on sampled solutions of the problem to improve the efficiency of derivative-free nonlinear programming solvers. The approach is tested on 24 single-objective functions to reduce the domain of decision variables. The results demonstrate better accuracy and consistency in the solver performance. Incorporating a machine learning technique into an optimization method can be extended to solve black-box optimization problems and paves the way for innovative solutions in engineering design and other domains.

关键词： Optimization Regression tree nonlinear programming Derivative-free method

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A nonlinear programming algorithm for finite element limit analysis using feasible arc searching technique

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INTERNATIONAL JOURNAL FOR NUMERICAL METHODS IN ENGINEERING 2023年第22期124卷 5102-5119页

作者： Zhang, Rui Xiao, Yao Changsha Univ Coll Civil Engn Changsha Peoples R China Guangdong Univ Technol Sch Civil & Transportat Engn Guangzhou Peoples R China Guangdong Univ Technol Sch Civil & Transportat Engn Guangzhou 510006 Peoples R China

This article presents a nonlinear programming algorithm for finite element limit analysis (FELA) based on feasible arc searching technique (FAST). The proposed algorithm has the potential to significantly reduce the iteration numbers required for convergence, making it a valuable tool for solving complex optimization problems from FELA. The algorithm also introduces several new features to the existing methods, including: (i) a novel method for determining a reasonable updating step length;(ii) the avoidance of solving an additional "phase one problem" for finding an initial feasible point;and (iii) the proposition of an empirical criterion for detecting infeasibility problems. The effectiveness of the proposed approach has been demonstrated through several classic examples derived from geotechnical engineering. The initial two examples show the superior convergence speed of the novel approach compared to existing methods. Additionally, the third example highlights the efficacy of the feasibility detection criterion for problems involving both prescribed and unknown external forces.

关键词： limit analysis finite element nonlinear programming interior point algorithm feasible arc searching technique feasibility detection criterion

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A thermal error modeling method for CNC lathes based on thermal distortion decoupling and nonlinear programming

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INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY 2023年第5-6期128卷 2599-2612页

作者： Du, Hongyang Jiang, Gedong Tao, Tao Hou, Ruisheng Yan, Zongzhuo Mei, Xuesong Xi An Jiao Tong Univ Sch Mech Engn Xianning West Rd Xian 710049 Peoples R China State Key Lab Mfg Syst Engn Xianning West Rd Xian 710049 Peoples R China Hebei Univ Engn Sch Mech & Equipment Engn Taiji Rd Handan 056038 Peoples R China Shaanxi Key Lab Intelligent Robots Xianning West Rd Xian 710049 Peoples R China

CNC lathes often use hydraulic systems or motors to lock the turret, which causes the turret to have a high temperature rise and a large thermal deformation. The traditional measuring method couples the thermal distortions of the spindle and the turret together, which is not conducive to establishing the thermal error model. To solve this problem, a new measuring method was used in this research to decouple the thermal linear and angular distortions of the spindle and the turret. In addition, constraints on the model coefficients were proposed by studying the effects of long-term and short-term variations in ambient temperature on the thermal deformation of machine tools, thus transforming the thermal deformation modeling of the spindle and turret into nonlinear programming problems. After building the thermal deformation models of the spindle and the turret, the thermal distortion model of the whole machine tool was obtained by combining them. Finally, three experiments were designed to verify the validity of the established models, and the models were compared with those established using conventional methods. The experimental results showed that the models built based on thermal distortion decoupling and nonlinear programming had higher accuracy and robustness.

关键词： CNC lathe Thermal distortion decoupling nonlinear programming Thermal error modeling

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An inexact projected gradient method with rounding and lifting by nonlinear programming for solving rank-one semidefinite relaxation of polynomial optimization

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MATHEMATICAL programming 2023年第1-2期201卷 409-472页

作者： Yang, Heng Liang, Ling Carlone, Luca Toh, Kim-Chuan NVIDIA Res Santa Clara CA USA Natl Univ Singapore Dept Math Singapore Singapore MIT Lab Informat & Decis Syst Cambridge MA USA Natl Univ Singapore Dept Math Singapore Singapore Natl Univ Singapore Inst Operat Res & Analyt Singapore Singapore

We consider solving high-order and tight semidefinite programming (SDP) relaxations of nonconvex polynomial optimization problems (POPs) that often admit degenerate rank-one optimal solutions. Instead of solving the SDP alone, we propose a new algorithmic framework that blends local search using the nonconvex POP into global descent using the convex SDP. In particular, we first design a globally convergent inexact projected gradient method (iPGM) for solving the SDP that serves as the backbone of our framework. We then accelerate iPGM by taking long, but safeguarded, rank-one steps generated by fast nonlinear programming algorithms. We prove that the new framework is still globally convergent for solving the SDP. To solve the iPGM subproblem of projecting a given point onto the feasible set of the SDP, we design a two-phase algorithm with phase one using a symmetric Gauss-Seidel based accelerated proximal gradient method (sGS-APG) to generate a good initial point, and phase two using a modified limited-memory BFGS (L-BFGS) method to obtain an accurate solution. We analyze the convergence for both phases and establish a novel global convergence result for the modified L-BFGS that does not require the objective function to be twice continuously differentiable. We conduct numerical experiments for solving second-order SDP relaxations arising from a diverse set of POPs. Our framework demonstrates state-of-the-art efficiency, scalability, and robustness in solving degenerate SDPs to high accuracy, even in the presence of millions of equality constraints.

关键词： Semidefinite programming Polynomial optimization Inexact projected gradient method Rank-one solutions nonlinear programming Degeneracy

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A spline smoothing homotopy method for nonlinear programming problems with both inequality and equality constraints

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MATHEMATICAL METHODS OF OPERATIONS RESEARCH 2023年第3期98卷 411-433页

作者： Dong, Li Zhou, Zhengyong Yang, Li Dalian Minzu Univ Sch Sci 31 Jinshi RdJinshitan Tourist Resort Dalian 116650 Liaoning Peoples R China Shanxi Normal Univ Sch Math & Comp Sci 339 Taiyu Rd Linfen 041000 Shanxi Peoples R China Dalian Univ Technol Sch Math Sci 2 Dagong Rd Panjin 124221 Liaoning Peoples R China

In this paper, we construct a new spline smoothing homotopy method for solving general nonlinear programming problems with a large number of complicated constraints. We transform the equality constraints into the inequality constraints by introducing two parameters. Subsequently, we use smooth spline functions to approximate the inequality constraints. The smooth spline functions involve only few inequality constraints. In other words, the method introduces an active set technique. Under some weaker conditions, we obtain the global convergence of the new spline smoothing homotopy method. We perform numerical tests to compare the new method to other methods, and the numerical results show that the new spline smoothing homotopy method is highly efficient.

关键词： Spline function Homotopy method nonlinear programming Global convergence

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Privacy-Preserving and Secure Cloud Computing: A Case of Large-Scale nonlinear programming

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IEEE TRANSACTIONS ON CLOUD COMPUTING 2023年第1期11卷 484-498页

作者： Du, Wei Li, Ang Li, Qinghua Zhou, Pan Univ Arkansas Dept Comp Sci & Comp Engn Fayetteville AR 72701 USA Duke Univ Dept Elect & Comp Engn Durham NC 27708 USA Huazhong Univ Sci & Technol Sch Elect Informat & Commun Wuhan 430074 Peoples R China

The volume of data is increasing rapidly, which poses a great challenge for resource-constrained users to process and analyze. A promising approach for solving computation-intensive tasks over big data is to outsource them to the cloud to take advantage of the cloud's powerful computing capability. However, it also brings privacy and security issues since the data uploaded to the cloud may contain sensitive and private information which should be protected. In this article, we address this problem and focus on the privacy-preserving and secure outsourcing of large-scale nonlinear programming problems (NLPs) subject to both linear constraints and nonlinear constraints. Large-scale NLPs play an important role in the field of data analytics but have not received enough attention in the context of cloud computing. In our outsourcing protocol, we first apply a secure and efficient transformation scheme at the client side to encrypt the private information of the considered NLP. Then, we use the reduced gradient method and generalized gradient method at the server side to solve the transformed large-scale NLPs under linear constraints and nonlinear constraints, respectively. We provide security analysis of the proposed protocol, and evaluate its performance via a series of experiments. The experimental results show that our protocol can efficiently solve large-scale NLPs and save much time for the client, providing a great potential for real applications.

关键词： Cloud computing Servers Privacy Outsourcing Protocols Gradient methods Task analysis privacy security nonlinear programming

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Second-Order Symmetric Duality for Multiple Objectives nonlinear programming Under Generalizations of Cone-Preinvexity Functions

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JOURNAL OF SCIENTIFIC COMPUTING 2023年第1期95卷 1-15页

作者： Kassem, Mohamed Abd El-Hady Tanta Univ Fac Sci Math Dept Tanta Egypt

In this article, we present generalizations of the cone-preinvexity functions and study a pair of second-order symmetric solutions for multiple objective nonlinear programming problems under these generalizations of the cone-preinvexity functions. In addition, we establish and prove the theorems of weak duality, strong duality, strict converse duality, and self-duality by assuming the skew-symmetric functions under these generalizations of the cone-preinvexity functions. Finally, we provide four nontrivial numerical examples to demonstrate that the results of the weak and strong duality theorems are true.

关键词： Multiple objective programming Second-order symmetric duality nonlinear programming Duality theorems Cones Preinvexity functions 90-XX 90CXX

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nonlinear programming Strategy for the dynamic spinning process optimization 34

Nonlinear Programming Strategy for the dynamic spinning proc...

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34th Chinese Control and Decision Conference (CCDC)

作者： Qiu, Junhao Wang, Tong Hao, Kuangrong Minist Educ Engn Res Ctr Digitized Textile & Apparel Technol Shanghai 201620 Peoples R China Donghua Univ Coll Informat Sci & Technol Shanghai 201620 Peoples R China

ISBN: (纸本)9781665478960

The polyester fiber spinning process is extensive, and the involved dynamic models are complex and difficult to solve analytically. This paper presents the optimal control strategy of polyester fiber production. Based on a dynamic model of the spinning process, optimal objectives are determined to minimize production costs and meet production goals. We employ Radau collocation on finite elements to discretize the continuous dynamic model, which is transformed into finite-dimensional nonlinear programming (NLP) model. The developed strategy combines a multiple shot algorithm and half-score method to solve the obtained NLP model. We find the best initial values quickly and achieve the control objective under the constraints of the spinning process's mechanism equations. The objective function measures the difference between the actual production and the desired value, and the merit of the objective function directly affects the control variables. In this paper, single-objective and multi-objective control strategies are designed according to different production requirements. To make the objective function effectively reflect the product quality, its setting is inseparable from each state variable, and we explore the control effect for different objective functions. Finally, simulation results verify the feasibility and superiority of the orthogonal collocation on finite element and multiple shooting algorithms.

关键词： polyester fibers dynamic optimization nonlinear programming Radau orthogonal collocation

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