So, the curious thing about recursion is that pretty much always can you implement this idea of doing the same thing again and again and again but with smaller bytes each time.
可见,递归算法中新奇的一点是,为了实现一个想法,你可以一遍又一遍地做相同的事情,但每次的规模都会有所减小。
We are expecting to see that it decreases because it's feeling a stronger pull, all the electrons are being pulled in closer to the nucleus, so that atomic size is going to get smaller.
我们将看到它是减小的,因为电子会感受到越来越强的吸引力,所有的电子将会被原子核拉得越来越近,所以原子半径将越来越小。
And it's just gathering together the multiplications while counting down the exponent. And you can see it when we get down to the end test here, we're going to pop out of there and we're going to return the answer.
这个方法就是通过乘法,来一个一个的减小指数,可以看到,最后面的结果测试,我们会在这里退出,然后返回答案。
OK, what we'll see shortly is that this will allow us to see that for an isolated system the entropy never decreases.
好,我们过一会,会看到,这将导致孤立系统的熵,永远不会减小。
And note that as Z increases, as the proton number increases the radius decreases for a given n number.
并注意到当Z不断增加,对于一个给定的n,即当质子数增加的时候,半径的n值就减小了。
Is that something that increases the badness of death, ? or does it reduce its significance somewhat?
这是否是某种增加了死亡坏处的事物,或者是否多少减小了些死亡的重要性?
And if you have women marrying off into other families, and then they leave the household of their fathers, and they are officially and legally in a household with somebody else, that may end up increasing those households that have intermarriage coming in and not so much intermarriage going out.
如果女子出嫁,就离开父亲家户,法律上正式加入丈夫家户,那么最终有通婚娶入的家户,会逐渐壮大,通婚嫁出的会逐渐减小。
The exact shape of the curve is subject to discussion, but the point of diminishing marginal utility is that, as you get more and more money, the increment in utility for each extra dollar diminishes.
这条曲线的确切形状还有待讨论,但是边际效用递减规律的重点在于,你得到的钱越多,每额外的一美元的增长效用会相对减小
Not only are we taking away an electron here, but we're also going to decrease shielding, so the electrons that are already in there are going to feel a higher z effective and will be pulling and the atom will be getting smaller.
这不只是因为我们拿走了一个电子,还因为我们这样做会减小屏蔽效应,这样留下的电子,将会感受到更大的有效核电量,也就会感受到更强的吸引力,使得原子变得更小。
And they do that to localize the source of the problem.
他们会不断的减小,寻找空间来定位问题源。
The force is not quite as strong as it was without this attractive force.
这样墙受到冲力也随之减小,于是实际的。
Typical characterization, not all the time, but typical characterization, is an algorithm that reduces the size of a problem by one, or by some constant amount each time, is typically an example of a linear algorithm.
我们学习过了线性算法,它的典型特征,不是通用的,但是比较典型的特征是,它是逐一减小问题的大小的,或者说是每次减小常数的大小。
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