Weak greedy algorithms[*]This research was supported by National Science Foundation Grant DMS 9970326 and by ONR Grant N00014‐96‐1‐1003.

被引:1
作者
V.N. Temlyakov
机构
[1] University of South Carolina,Department of Mathematics
来源
Advances in Computational Mathematics | 2000年 / 12卷
关键词
Greedy Algorithm; Nonlinear Approximation; Projection Pursuit; Greedy Approximation; Projection Pursuit Regression;
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学科分类号
摘要
Theoretical greedy type algorithms are studied: a Weak Greedy Algorithm, a Weak Orthogonal Greedy Algorithm, and a Weak Relaxed Greedy Algorithm. These algorithms are defined by weaker assumptions than their analogs the Pure Greedy Algorithm, an Orthogonal Greedy Algorithm, and a Relaxed Greedy Algorithm. The weaker assumptions make these new algorithms more ready for practical implementation. We prove the convergence theorems and also give estimates for the rate of approximation by means of these algorithms. The convergence and the estimates apply to approximation from an arbitrary dictionary in a Hilbert space.
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页码:213 / 227
页数:14
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