We discuss several different definitions of directional derivative and gradient vector.

摘要讨论了几种不同的方向导数和梯度的定义.

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Derivatives in higher dimensions , directional derivative and gradient.

多元微分 、 方向导数和梯度.

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Use of signal joint probability of directional derivative entropy minimum get the best rotation angle!

利用信号联合概率的方向导数熵最小获得最佳的旋转角度!

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Secondly, the new generalized gradient is introduced to take advantage of the given directional derivative.

其次讨论了一类新的广义梯度,这样的广义梯度能够充分利用已经有的方向导数的信息.

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