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A Two-stage Adaptive Robust Model for Residential Micro-CHP Expansion Planning

Journal of Modern Power Systems and Clean Energy 2021年第4期9卷 826-836页

作者： Fatemeh Teymoori Hamzehkolaei Nima Amjady Bahareh Bagheri Department of Electrical Engineering Semnan UniversitySemnanIran

This paper addresses the planning problem of residential micro combined heat and power (micro-CHP) systems (including ‎micro-generation units, auxiliary boilers, and thermal storage tanks) considering the associated technical and economic factors. Since the accurate values of the thermal and electrical loads of this system cannot be exactly predicted for the planning horizon, the thermal and electrical load uncertainties are modeled using a two-stage ‎adaptive robust optimization method based on a ‎polyhedral uncertainty set. A solution method, which is composed of column-and-constraint generation (C&CG) algorithm ‎and block coordinate descent (BCD) method, is proposed to efficiently solve this adaptive robust optimization model. Numerical results ‎from a practical case study show the effective performance of the proposed adaptive robust model for residential micro-CHP planning and its solution method.

关键词： Micro combined heat and power(micro-CHP)planning two-stage adaptive robust optimization model block coordinate descent method polyhedral uncertainty set

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Joint Optimization for on-Demand Deployment of UAVs and Spectrum Allocation in UAVs-Assisted Communication

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China Communications 2024年第7期21卷 278-290页

作者： Chen Yong Liao Naiwen WangWei Zhang Xianyu Zhang Yu College of Communication Engineering Army Engineering University of PLANanjing 210007China The Sixty-Third Research Institute National University of Defense TechnologyNanjing 210007China

To improve the efficiency and fairness of the spectrum allocation for ground communication assisted by unmanned aerial vehicles(UAVs),a joint optimization method for on-demand deployment and spectrum allocation of UAVs is proposed,which is modeled as a mixed-integer non-convex optimization problem(MINCOP).An algorithm to estimate the minimum number of required UAVs is firstly proposed based on the pre-estimation and simulated *** MINCOP is then decomposed into three sub-problems based on the block coordinate descent method,including the spectrum allocation of UAVs,the association between UAVs and ground users,and the deployment of ***,the optimal spectrum allocation is derived based on the interference mitigation and channel *** association between UAVs and ground users is optimized based on local iterated optimization.A particle-based optimization algorithm is proposed to resolve the subproblem of the UAVs *** results show that the proposed method could effectively improve the minimum transmission rate of UAVs as well as user fairness of spectrum allocation.

关键词： block coordinate descent method on-demand deployment spectrum allocation UAVs-assisted Communication

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On the Linear Convergence of the Approximate Proximal Splitting method for Non-smooth Convex Optimization

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Journal of the Operations Research Society of China 2014年第2期2卷 123-141页

作者： Mojtaba Kadkhodaie Maziar Sanjabi Zhi-Quan Luo University of Minnesota MinneapolisMNUSA

Consider the problem of minimizing the sum of two convex functions,one being smooth and the other *** this paper,we introduce a general class of approximate proximal splitting(APS)methods for solving such minimization *** in the APS class include many well-known algorithms such as the proximal splitting method,the block coordinate descent method(BCD),and the approximate gradient projection methods for smooth convex *** establish the linear convergence of APS methods under a local error bound *** the latter is known to hold for compressive sensing and sparse group LASSO problems,our analysis implies the linear convergence of the BCD method for these problems without strong convexity assumption.

关键词： Convex optimization Proximal splitting method block coordinate descent method Convergence rate analysis Local error bound

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A Parallel Line Search Subspace Correction method for Composite Convex Optimization

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Journal of the Operations Research Society of China 2015年第2期3卷 163-187页

作者： Qian Dong Xin Liu Zai-Wen Wen Ya-Xiang Yuan State Key Laboratory of Scientific and Engineering Computing Academy of Mathematics and Systems ScienceChinese Academy of SciencesBeijingChina Beijing International Center for Mathematical Research Peking UniversityBeijingChina

In this paper,we investigate a parallel subspace correction framework for composite convex *** variables are first divided into a few blocks based on certain *** each iteration,the algorithms solve a suitable subproblem on each block simultaneously,construct a search direction by combining their solutions on all blocks,then identify a new point along this direction using a step size satisfying the Armijo line search *** are called PSCLN and PSCLO,respectively,depending on whether there are overlapping regions between two imme-diately adjacent blocks of *** convergence is established under mild *** compare PSCLN and PSCLO with the parallel version of the fast iterative thresholding algorithm and the fixed-point continuation method using the Barzilai-Borwein step size and the greedy coordinate block descent method for solving the l1-regularized minimization *** numerical results showthatPSCLN andPSCLOcan run fast and return solutions notworse than those from the state-of-theart algorithms on most test *** is also observed that the overlapping domain decomposition scheme is helpful when the data of the problem has certain special structures.

关键词： Line search block coordinate descent method Domain decomposition Jacobian-type iteration Distributed optimization

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