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Low-rank matrix recovery (MR) has been widely used in data analysis and dimensionality reduction. As a direct heuristic to MR, convex relaxation is usually degraded by the repeated calling of singular value decomposition (SVD), especially in large-scale applications. In this paper, we propose a novel Riemannian optimization method (ROAM) for MR problem by exploiting the Riemannian geometry of the...
In this paper, a new improved SQP algorithm which can avoid Maratos effect is proposed to solve minimax problems with inequality constraints. Compared with the existing methods, the computational effort is reduced. In addition, its global and superlinear convergence are obtained under some mild assumptions. Finally, some numerical results show that the method is feasible and effective.
EP (evolutionary programming), ES (evolutionary strategy) and GA (genetic algorithm) are three approaches of optimization inspired by the natural evolution process; they are essentially much more in common in terms of computing models and algorithms. A general evolutionary algorithm is proposed and its convergence properties are analyzed. It is claimed that if there exist some quasi-stable states...
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