Complexity analysis for optimization methods
نویسندگان
چکیده
منابع مشابه
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In many machine learning problems such as the dual form of SVM, the objective function to be minimized is convex but not strongly convex. This fact causes difficulties in obtaining the complexity of some commonly used optimization algorithms. In this paper, we proved the global linear convergence on a wide range of algorithms when they are applied to some non-strongly convex problems. In partic...
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*Correspondence: [email protected] Department of Applied Mathematics, Pukyong National University, Busan, 608-737, Korea Abstract A linear optimization problem over a symmetric cone, defined in a Euclidean Jordan algebra and called a self-scaled optimization problem (SOP), is considered. We formulate an algorithm for a large-update primal-dual interior-point method (IPM) for the SOP by using a p...
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In many machine learning problems such as the dual form of SVM, the objective function to be minimized is convex but not strongly convex. This fact causes difficulties in obtaining the complexity of some commonly used optimization algorithms. In this paper, we proved the global linear convergence on a wide range of algorithms when they are applied to some non-strongly convex problems. In partic...
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ژورنال
عنوان ژورنال: SCIENTIA SINICA Mathematica
سال: 2020
ISSN: 1674-7216
DOI: 10.1360/n012018-00251