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Guoqiang Wang
Researcher at Shanghai University of Engineering Sciences
Publications - 41
Citations - 804
Guoqiang Wang is an academic researcher from Shanghai University of Engineering Sciences. The author has contributed to research in topics: Interior point method & Computer science. The author has an hindex of 17, co-authored 33 publications receiving 691 citations. Previous affiliations of Guoqiang Wang include Shanghai University.
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A New Full Nesterov–Todd Step Primal–Dual Path-Following Interior-Point Algorithm for Symmetric Optimization
Guoqiang Wang,Yanqin Bai +1 more
TL;DR: This paper generalizes a primal–dual path-following interior-point algorithm for linear optimization to symmetric optimization by using Euclidean Jordan algebras and derives the currently best known iteration bound for the small-update method.
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A new primal-dual path-following interior-point algorithm for semidefinite optimization
TL;DR: The currently best known iteration bound for the algorithm with small-update method is obtained, namely, O ( n log n ϵ ) , which is as good as the linear analogue.
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Primal-Dual Interior-Point Algorithms for Semidefinite Optimization Based on a Simple Kernel Function
TL;DR: This paper presents a primal-dual interior-point algorithm for SDO problems based on a simple kernel function which was first presented at the Proceedings of Industrial Symposium and Optimization Day, Australia, November 2002; the function is not self-regular.
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A Class of Large-Update and Small-Update Primal-Dual Interior-Point Algorithms for Linear Optimization
TL;DR: A class of polynomial primal-dual interior-point algorithms for linear optimization based on a new class of kernel functions that includes the classical logarithmic function, the prototype self-regular function, and non-self-regular kernel functions as special cases.
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Alternating Direction Method of Multipliers for Separable Convex Optimization of Real Functions in Complex Variables
Lu Li,Xingyu Wang,Guoqiang Wang +2 more
TL;DR: The numerical simulations on the reconstruction of electroencephalogram (EEG) signal are provided to show that the new ADMM has better behavior than the classic ADMM for solving separable convex optimization of real functions in complex variables.