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Exploiting the Propagation of Constrained Variables for Enhanced HDX-MS Data Optimization

ANALYTICAL CHEMISTRY 2021年第49期93卷 16417-16424页

作者： Salmas, Ramin Ekhteiari Borysik, Antoni James Kings Coll London Dept Chem Britannia House London SE1 1DB England

nonlinear programming has found useful applications in protein biophysics to help understand the microscopic exchange kinetics of data obtained using hydrogen-deuterium exchange mass spectrometry (HDX-MS). Finding a microscopic kinetic solution for HDX-MS data provides a window into local protein stability and energetics allowing them to be quantified and understood. Optimization of HDX-MS data is a significant challenge, however, due to the requirement to solve a large number of variables simultaneously with exceptionally large variable bounds. Modeled rates are frequently uncertain with an explicate dependency on the initial guess values. In order to enhance the search for a minimum solution in HDX-MS optimization, the ability of selected constrained variables to propagate throughout the data is considered. We reveal that locally bound constrained optimization induces a global effect on all variables. The global response to local constraints is large and surprisingly long-range, but the outcome is unpredictable, unexpectedly decreasing the overall accuracy of certain data sets depending on the stringency of the constraints. Utilizing previously described in-house validation criteria based on covariance matrices, a method is described that is able to accurately determine whether constraints benefit or impair the optimization of HDX-MS data. From this, we establish a new two-stage method for our online optimizer HDXmodeller that can effectively leverage locally bound variables to enhance HDX-MS data modeling.

关键词： Covariance matrix Constrained optimization Mathematical optimization Protein folding nonlinear programming

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Model predictive control for on-off charging of electrical vehicles in smart grids

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IET ELECTRICAL SYSTEMS IN TRANSPORTATION 2021年第2期11卷 121-133页

作者： Shi, Ye Tuan, Hoang D. Savkin, Andrey V. Poor, H. Vincent Shanghaitech Univ Sch Informat Sci & Technol Shanghai 201210 Peoples R China Univ Technol Sydney Sch Elect & Data Engn Broadway NSW Australia Univ New South Wales Sch Elect Engn & Telecommun Sydney NSW Australia Princeton Univ Dept Elect Engn Princeton NJ 08544 USA

Over the next decade, a massive number of plug-in electric vehicles (PEVs) will need to be integrated into current power grids. This is likely to give rise to unmanageable fluctuations in power demand and unacceptable deviations in voltage. These negative impacts are difficult to mitigate because PEVs connect and disconnect from the grid randomly and each type of PEVs has different charging profiles. This paper presents a solution to these problems that involves coordination of power grid control and PEV charging. The proposed strategy minimises the overall costs of charging and power generation in meeting future increases in PEV charging demand and the operational constraints of the power grid. The solution is based on an on-off PEV charging strategy that is easy and convenient to implement online. The joint coordination problem is formulated by a mixed integer non-linear programming (MINP) with binary charging and continuous voltage variables and is solved by a highly novel computational algorithm. Its online implementation is based on a new model predictive control method that is free from prior assumptions about PEVs' arrival and charging information. Comprehensive simulations are provided to demonstrate the efficiency and practicality of the proposed methods.

关键词： electrical vehicles power demand different charging profiles battery powered vehicles current power grids power grid control charging power generation joint coordination problem binary charging plug-in electric vehicles unmanageable fluctuations smart grids smart power grids linear programming nonlinear programming PEV charging strategy integer programming predictive control model predictive control method electric vehicles PEVs' arrival power grids unacceptable deviations

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Adaptive trust-region algorithms for unconstrained optimization

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OPTIMIZATION METHODS & SOFTWARE 2021年第5期36卷 1059-1081页

作者： Rezapour, Mostafa Asaki, Thomas J. Washington State Univ Dept Math & Stat Pullman WA 99164 USA

In this paper, we propose two trust-region algorithms for unconstrained optimization. The trust-region algorithms minimize a model of the objective function within the trust-region, next update the size of the region and then repeat the procedure to find a first-order stationary point for the objective function. The size of the trust-region at each step is very critical to the effectiveness of the algorithm, particularly for large-scale problems, because minimizing the model at each step needs the gradient and the Hessian information of the objective function. Our modified trust-region algorithms are opportunistic in the sense that they explore beyond the trust-region if the boundary of the region prevents the algorithm from accepting a more beneficial point. It occurs when there is a very good agreement between the model and the objective function on the trust-region boundary, and we can find a step outside the trust-region with smaller value of the model while at which the agreement between the model and the objective function remains good. We show that the algorithms are convergent. Initial numerical experiments show that the proposed algorithms are more efficient than the traditional trust-region algorithm for a large majority of problems in the CUTEst suite.

关键词： Unconstrained optimization trust-region methods nonlinear programming

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Movable Antenna Enhanced MEC Systems with Discrete Antenna Position Selection

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IEEE Communications Letters 2025年

作者： Cang, Yihan Chen, Ming Yang, Zhaohui Southeast University National Mobile Communications Research Laboratory Nanjing210096 China Purple Mountain Laboratories Nanjing211100 China Zhejiang University College of Information Science and Electronic Engineering Hangzhou310027 China Zhejiang University International Joint Innovation Center Haining314400 China Zhejiang Provincial Key Laboratory of Info. Proc Hangzhou310027 China

In this work, movable antenna (MA) is invoked for enhancing the task completion performance of a mobile edge computing (MEC) system. Specifically, in the considered system model, a base station equipped with MAs assists in computing tasks offloaded from multiple devices. In contrast to previous works focusing on continuous antenna movement, a more realistic scenario involving discrete antenna position selection is taken into account under the constraint of minimum antenna distance. To maximize the total task completion rate of all devices, it is essential to jointly optimize the transmit power and computation rate at each device as well as MA position selection while satisfying power and discrete antenna selection constraints. To address this challenging mixed-integer nonlinear programming problem, we first reformulate the original problem into an equivalent, more tractable form by Lagrangian dual transform. Then a penalty dual decomposition based method is proposed to efficiently find the stationary solutions. Simulation results confirm that MA-assisted MEC systems significantly outperform the conventional fixed-position antenna and antenna selection based schemes. © 1997-2012 IEEE.

关键词： nonlinear programming

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UNMANNED AERIAL VEHICLE'S TRAJECTORY OPTIMIZATION IN CONSTRAINED ENVIRONMENTS 33

UNMANNED AERIAL VEHICLE'S TRAJECTORY OPTIMIZATION IN CONSTRA...

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33rd Congress of the International Council of the Aeronautical Sciences, ICAS 2022

作者： Novák, Jiří Chudý, Peter Faculty of Information Technology Brno University of Technology Czech Republic

ISBN: (纸本)9781713871163

The range of multirotor Unmanned Aerial Vehicle (UAV) applications has grown significantly over the last decade. This is to be attributed to their simple mechanical design, along with hovering and maneuvering capabilities, making them a popular choice in applications such as surveillance, aerial photography, cargo transport or infrastructure inspection. Varying mission requirements with respect to UAV target location, payload capacity, speed or time of flight combined with environmental constraints such as no-fly zones can be hard to satisfy without the use of modern trajectory optimization techniques. Trajectory optimization problems are often formulated using optimal control theory. An Optimal Control Problem (OCP) for generalized multirotor UAV introducing environmental constraints has been formulated and solved by a direct transcription approach. Furthermore, a Gauss pseudospectral method has been implemented, enabling to design a framework for UAV trajectory optimization problems in constrained environments. © 2022 ICAS. All Rights Reserved.

关键词： nonlinear programming

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Game-Theoretic Power Allocation and Client Selection for Privacy-Preserving Federated Learning in IoMT

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IEEE Transactions on Communications 2025年

作者： Liu, Jingyuan Chang, Zheng Ye, Chaoxiong Mumtaz, Shahid Hamalainen, Timo University of Electronic Science and Technology of China School of Computer Science and Engineering Chengdu611731 China University of Jyväskylä Department of Psychology 35 JyväskyläFIN-40014 Finland Nottingham Trent University Department of Computer Science United Kingdom University of Jyväskylä Faculty of Information Technology 35 JyväskyläFIN-40014 Finland

In recent years, the Internet of Medical Things (IoMT) has significantly boosted the healthcare industry. Federated learning (FL) can enhance the utilization of patient data while protecting privacy. Despite the great potential of FL to enhance the architecture of IoMT, the need for effective interference management and the limited energy resources of IoMT devices make the integration of FL into IoMT environments particularly challenging. This study proposes an innovative framework to address these challenges by optimizing power allocation and client selection across participating IoMT devices in the FL process. By employing a Stackelberg game model, our approach orchestrates power allocation among IoMT devices to enhance communication efficiency while adhering to strict differential privacy (DP) standards. Regarding the availability of network state information, we propose non-uniform pricing and uniform pricing strategies, respectively. Then, we derive the optimal interference price and power for the IoMT devices using nonlinear programming and convex optimization. To tackle the issue of energy constraints in IoMT devices, we adopt Lyapunov optimization for adaptive client selection, ensuring sustainable device participation in the FL process over time. In addition, our approach integrates DP to protect patient data, carefully balancing between privacy and the accuracy of the learning model. Our extensive simulations demonstrate marked improvements in privacy preservation, communication efficiency, and energy management efficiency, highlighting the effectiveness of our proposed method over existing solutions. © 1972-2012 IEEE.

关键词： nonlinear programming

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JPUSA in Coexistence of eMBB and URLLC Services in Multi-Cell IRS-Assisted Terahertz Networks

IEEE Transactions on Green Communications and Networking

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IEEE Transactions on Green Communications and Networking 2024年

作者： Rahim, Muddasir Nguyen, Thanh Luan Kaddoum, Georges MontréalQCH3C 1K3 Canada

In this paper, we examine coexistence of enhanced mobile broadband (eMBB) and ultra-reliable low-latency communication (URLLC) services within an intelligent reconfigurable surface (IRS)-assisted terahertz multi-cell network. The simultaneous operation of eMBB and URLLC services in the same network poses significant resource management (RM) challenges. To address this concern, we propose a joint power, user, and service allocation (JPUSA) framework. This optimization, which is a multi-objective optimization problem, aims to maximize the eMBB data rate while ensuring URLLC reliability. It is a challenging NP-hard mixed-integer nonlinear programming problem. We propose a weighted sum method to convert it into a single-objective optimization problem, decomposing it into eMBB and URLLC RM sub-problems. Specifically, a one-to-many matching game is introduced to allocate IRSs to eMBB users, and a puncturing technique is used to assign eMBB resources to URLLC users. Simulation results reveal a superior performance of the proposed scheme over baseline methods, particularly in terms of the eMBB sum data rates and URLLC reliability. Additionally, the proposed algorithm's sum rate for eMBB users closely approximates that of an exhaustive search (ES) method. The results of our complexity analysis demonstrate that the proposed scheme has a lower computational complexity as compared to the ES scheme. © 2017 IEEE.

关键词： nonlinear programming

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Incentive rate determination in viral marketing

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2021年第3期289卷 1169-1187页

作者： Tavasoli, Ali Shakeri, Heman Ardjmand, Ehsan Young, William A., II Payame Noor Univ Dept Mech Engn Tehran Iran Univ Virginia Sch Data Sci Charlottesville VA USA Ohio Univ Coll Business Dept Analyt & Informat Syst Athens OH 45701 USA

In viral marketing campaigns, incentivized consumers can act as sales agents by sharing information. In this study, we investigate the problem of incentive rate determination over a network of consumers to maximize the profit of a single good by a monopolist. For this purpose, we develop an epidemic spreading model to explore the dynamics of a viral marketing campaign under network externalities and incentivized individuals. We will examine two cases of homogeneous and heterogeneous incentive rates. In each case, we derive an N-intertwined dynamics model and obtain the existence and stability conditions of a trade-free or an endemic equilibrium. By treating the incentive as a control parameter, we investigate the problem of maximizing the monopolist's profit by formulating two nonlinear programming models. In the case of homogeneous incentive rates, results show that the optimal incentive is determined by devising a balance between the consumers' states in the Markov process. In the heterogeneous case, it is observed that despite the existence of a strong correlation with different centrality measures, the optimal incentive allocation cannot be solely determined by centrality measures. (C) 2020 Elsevier B.V. All rights reserved.

关键词： OR in marketing Incentive rate determination Dynamical systems over networks nonlinear programming Epidemic spread

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Multi-objective Stochastic Volt/VAR Optimization in AC-DC Hybrid Distribution Network Considering Soft Open Point

Multi-objective Stochastic Volt/VAR Optimization in AC-DC Hy...

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International Conference on Smart Grid Energy Systems and Control, SGESC 2021

作者： Pamshetti, Vijay Babu Murty, V.V.S.N. Singh, S.P. Sharma, Ashwani Kumar Chaitanya Bharathi Institute of Technology Telangana Hyderabad500075 India National Institute of Technology Kurukshetra Haryana Kurukshetra136119 India Varanasi Uttar Pradesh Varanasi221005 India

ISBN: (纸本)9789811674716

To cope with different types of distributed energy sources (DERs) and AC/DC loads, combined AC and DC distribution network has emerged as a potential solution for the forthcoming distribution network. However, upward integration of DERs imposes several problems such as voltage violations and system power losses. In order to minimize the system power loss and voltage deviations at the same time, a two-stage multi-objective stochastic Volt/VAR optimization for HDN considering soft open points (SOP) is proposed in this paper. In state 1, a coordination optimization model considering legacy Volt/VAR control devices, status of network switches, DERs, voltage source converter (VSC) and SOP has been established;in stage 2, model predictive control (MPC) based rolling optimization control model has been built, to eliminate the forecasted deviations caused by renewable energy sources and loads. Besides, the original non-convex mixed-integer nonlinear programming (MINLP) problem has been transformed into a mixed-integer second-order cone programming (MISOCP) problem for a feasible solution. The proposed scheme has been verified and authenticated on IEEE 33 bus AC-DC DN. The simulation results reveal the reputation of the proposed methodology on the aforementioned problems. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： nonlinear programming

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Reliable cross-docking location problem under the risk of disruptions

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OPERATIONAL RESEARCH 2021年第3期21卷 1569-1612页

作者： Hasani Goodarzi, Asefeh Zegordi, Seyed Hessameddin Alpan, Gulgun Nakhai Kamalabadi, Isa Husseinzadeh Kashan, Ali Univ Quebec Dept Syst Engn Ecole Technol Super ETS POB H3C 1K3 Montreal PQ Canada Tarbiat Modares Univ Fac Ind & Syst Engn POB 14115-143 Tehran Iran Univ Grenoble Alpes CNRS Grenoble INP G SCOP F-38000 Grenoble France

This paper seeks to develop a reliable network of cross-docks by taking in to account disruption and reliability issues to hedge against heterogeneous risk of cross-docking failure. In real environments, applying a recovery policy can be a feasible strategy to handle disruptions. Hence, in this study, a recovery policy has been addressed in the form of reallocating suppliers to alternative cross-docks or altering the transportation strategy to move shipments. In addition to cross-dock location design, the optimum capacity of opened cross-docks will be determined considering the loads that will be served by each cross-docking center under regular and disruption conditions. A mixed integer nonlinear programming formulation is presented for the problem and is then linearized to present an efficient model. In order to solve it, two Lagrangian relaxation algorithms are designed and tested on 40 problem instances with different values of parameters. The results achieved by GAMS/CPLEX are compared with those of two algorithms and some analyses are performed on the solutions. Moreover, as the case study, the focus has been placed on logistic part of a car-manufacturing company with a vast supply chain network, containing more than 600 suppliers. The logistic strategies have been applied in order to reduce the transportation cost through the supply chain network and diminish the disruption subsequences in such a network. Based on the results, some managerial recommendations are presented.

关键词： Reliability Cross-dock location design Heterogeneous disruption nonlinear programming Lagrangian relaxation

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