We prove the finite convergence of CLAR-LASSO and analyze its time and space complexity.

我们证明了CLAR - LASSO的有限收敛性，并分析了它的时间复杂度和空间复杂度。

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In this paper we prove a finite convergence of online BP algorithms for nonlinear feedforward neural networks when the training patterns are linearly separable.

当训练样本线性可分时，本文证明前馈神经网络的在线BP算法是有限次收敛的。

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It is shown that this method possesses global convergence and the penalty parameters are adjusted only finite times under mild conditions.

在较为温和的条件下证明了方法的全局收敛性，及罚参数只需进行有限次调整。

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