如果对代码进行向量化，由于您正在将这些值当作字节进行处理，这意味着每条指令都要一次操作16 个值！
If you vectorize this code, since you are treating the values as bytes, that means that each instruction will operate on 16 values at once!
它们与标准正交笛卡尔向量相关。
谷歌的网页级别是特征向量中心性度量的一个变体。
Google's PageRank is a variant of the Eigenvector centrality measure.
用我们喜欢的向量场来点乘它。
现在，梯度向量是什么呢？
那相当于法向量指向上。
那意味着法向量指向下。
法向量指向上，这里我们知道它是什么意思。
故我们是把位置向量，分解为简单向量的和。
So, we want to decompose the position vector into a sum of simpler vectors.
那么向量在这，--，这向量是。
你用了法向量i，而不是竖直向量。
K instead of using the vertical vector k, you use the normal vector I.
它与向量场成比例。
这些则包括向量。
有一个向量场来描述每一个点上的向量。
We have a vector field that gives us a vector at every point.
所以，梯度向量是垂直的。
速度向量，是位置向量关于时间的导数。
So, the velocity vector is the derivative of a position vector with respect to time.
这是平面法向量，和沿直线向量的点积。
It's the dot product between the normal vector of a plane and the vector along the line.
一个法向量与任意常数相乘，还是法向量。
You can just multiply a normal vector by any constant, you will still get the normal vector.
指向和梯度向量相反。
事实上，给定的向量场与法向量是相互平行的。
In fact, our vector field and our normal vector are parallel to each other.
我们研究的是，向量场在曲线法向量方向的情况。
And we looked at the component of a vector field in the direction that was normal to the curve.
如果说函数的梯度是向量，那么向量场的散度就是函数。
So, if the gradient of a function is a vector, the divergence of a vector field is a function.
这门课我们首先学习的概念是向量，以及怎样做向量的内积。
So, the first things that we learned about in this class were vectors, and how to do dot-product of vectors.
当需要判断一个向量场是否保守向量场时,旋度也会派上用场的。
One place where it comes up is when we try to understand whether a vector field is conservative.
这把一个向量场的线积分，和另外一个向量场的曲面积分联系起来。
This relates a line integral for one field to a surface integral from another field.
最后，还要释放分配给向量的内存并把NULL向量返回给调用者。
Lastly, the memory allocated to the vector is also freed and NULL vector is returned to the caller.
然而，问题是向量处理会假设每条指令都可以适用于向量的所有元素。
However, the problem is that vector processing assumes that each and every instruction will be applied to all elements of the vector.
然而，问题是向量处理会假设每条指令都可以适用于向量的所有元素。
However, the problem is that vector processing assumes that each and every instruction will be applied to all elements of the vector.
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