Výsledky vyhľadávania - Linear-Constrained Convex Quadratic Programming

Generalized linear‐constrained optimal power flow for distribution networks Autor Li, Biao, Wan, Can, Li, Yunyi, Jiang, Yibao, Yu, Peng

“… This paper proposes a novel generalized linear‐constrained optimal power flow (GLOPF) model for both radial and mesh distribution networks, which iteratively approaches the optimal solution to the original OPF problem. The first…”
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Smart Charging of EVs in Residential Distribution Systems Using the Extended Iterative Method Autor Zhang, Jian, Cui, Mingjian, Fang, Hualiang, He, Yigang

“…Smart charging of electrical vehicles (EVs) is critical to provide the secure and cost-effective operation for distribution systems. Three model objective…”
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Sparse Recovery: The Square of ℓ1/ℓ2 Norms Autor Jia, Jianqing, Prater-Bennette, Ashley, Shen, Lixin, Tripp, Erin E.

“… We detail two accompanying algorithms based on Dinkelbach’s procedure and a difference of convex functions strategy…”
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A semi-smooth Newton method for a special piecewise linear system with application to positively constrained convex quadratic programming Autor Barrios, J.G., Bello Cruz, J.Y., Ferreira, O.P., Németh, S.Z.

“… As an application, we study the convex quadratic programming problem under positive constraints. The numerical results suggest that the semi-smooth Newton method achieves…”
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A PRECONDITIONING FRAMEWORK FOR SEQUENCES OF DIAGONALLY MODIFIED LINEAR SYSTEMS ARISING IN OPTIMIZATION Autor BELLAVIA, STEFANIA, DE SIMONE, VALENTINA, DI SERAFINO, DANIELA, MORINI, BENEDETTA

“… Such sequences arise in several optimization methods, e.g., in affine-scaling methods for bound-constrained convex quadratic programming and bound-constrained linear least…”
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A Global Solution Algorithm for AC Optimal Power Flow through Linear Constrained Quadratic Programming Autor Barati, Masoud

“…We formulate the Alternating Current Optimal Power Flow Problem (ACOPF) as a Linear Constrained Quadratic Program (LCQP…”
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On safe tractable approximations of chance constraints Autor Nemirovski, Arkadi

“… ► Lagrangian approximation of quadratically perturbed scalar linear inequalities. ► Approximation of affinely perturbed conic quadratic/semidefinite inequalities…”
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Task Scheduling for Mobile Edge Computing with Multiple Links Autor Yang, Lichao, Zhang, Heli, Ji, Hong, Li, Xi

“…As the explosive growth of smart devices and new applications, traffic volume has been growing exponentially. Thus, mobile edge computing (MEC) technology…”
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MPTV: Matching Pursuit-Based Total Variation Minimization for Image Deconvolution Autor Dong Gong, Mingkui Tan, Qinfeng Shi, van den Hengel, Anton, Yanning Zhang

“… We formulate the inhomogeneous TV minimization problem as a convex quadratic constrained linear programming problem…”
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Interior algorithms for linear, quadratic, and linearly constrained convex programming Autor Ye, Yinyu

“…: convex quadratic and linearly constrained convex programs. These algorithms are polynomial-time algorithms if the objective function is convex quadratic…”
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Sparse Recovery: The Square of $$\ell _1/\ell _2$$ Norms Autor Jia, Jianqing, Prater-Bennette, Ashley, Shen, Lixin, Tripp, Erin E.

“… We detail two accompanying algorithms based on Dinkelbach’s procedure and a difference of convex functions strategy…”
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Sparse Recovery: The Square of \(\ell_1/\ell_2\) Norms Autor Jia, Jianqing, Prater-Bennette, Ashley, Shen, Lixin, Tripp, Erin E

“… We detail two accompanying algorithms based on Dinkelbach's procedure and a difference of convex functions strategy…”
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A semi-smooth Newton method for a special piecewise linear system with application to positively constrained convex quadratic programming Autor Barrios, J G, Bello Cruz, J Y, Ferreira, O P, Németh, S Z

“… As an application, we study the convex quadratic programming problem under positive constraints. The numerical results suggest that the semi-smooth Newton method achieves…”
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Damped Proximal Augmented Lagrangian Method for weakly-Convex Problems with Convex Constraints Autor Dahal, Hari, Liu, Wei, Xu, Yangyang

“…We give a damped proximal augmented Lagrangian method (DPALM) for solving problems with a weakly-convex objective and convex linear/nonlinear constraints…”
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Robust adaptive beamforming based on response vector optimization Autor Jingwei Xu, Guisheng Liao, Shengqi Zhu

“… To generate a better response vector, it is first established as a non-convex quadratically constrained quadratic programming problem, and then is transformed into a semidefinite programming problem…”
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Control theoretic splines: optimal control, statistics, and path planning (princeton series in applied mathematics). Autor Egerstedt, Magnus, Martin, Clyde

“… This book unifies these constructions in a comprehensive and accessible way, drawing from the latest methods and applications to show how they arise naturally in the theory of linear control systems…”
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Hybrid DE-SQP and hybrid PSO-SQP methods for solving dynamic economic emission dispatch problem with valve-point effects Autor Elaiw, A.M., Xia, X., Shehata, A.M.

“…) problem taking into consideration valve-point effects is a complicated non-linear constrained multi-objective optimization problem with non-smooth and non-convex characteristics…”
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Algorithms for discrete nonlinear optimization in FICO Xpress Autor Belotti, Pietro, Berthold, Timo, Neves, Kelligton

“…), mixed integer linear programming (MIP), convex quadratic programming (QP), convex quadratically constrained quadratic programming (QCQP…”
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Fluence map optimization (FMO) with dose-volume constraints in IMRT using the geometric distance sorting method Autor Lan, Yihua, Li, Cunhua, Ren, Haozheng, Zhang, Yong, Min, Zhifang

“… In each iteration step, an interior point method is adopted to solve each new linear constrained quadratic programming…”
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An augmented Lagrangian method with constraint generation for shape-constrained convex regression problems Autor Lin, Meixia, Sun, Defeng, Toh, Kim-Chuan

“… . We prove that the least squares estimator is computable via solving an essentially constrained convex quadratic programming (QP) problem with ( d + 1 ) n variables, n ( n - 1…”
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