-
And now we want to find the subset of a that has the maximum value, subject to the weight constraint.
会有一个值与其对应,现在我们想要找出满足，重量约束条件的a的最大值子集。
-
So I've got a function to maximize and a constraint that must be obeyed.
一个需要得到最大值的函数,和一个需要遵守的约束条件。
-
STUDENT: Just a quick question on some of the constraints, like isothermal, isobaric, isovolumetric expansion.
学生：我有个关于,等温，等压，等容等约束条件的小问题。
-
Is to say, okay now let's try to enrich the model, to add more into the model, and see if you get a different result, and if so why?
好吧,那我们就来完善这个模型吧,给这个模型加入新的约束条件,看看会不会得到不同的结果,如果是再分析原因
-
And the constraint is what?
约束条件是什么呢？
-
So he's packing and unpacking, packing and unpacking, trying all possible combinations of objects that will obey the constraint. And then choosing the winner. Well, this is like an algorithm we've seen before. It's not greedy.
因此它不断装包和清包,尝试了所有满足约束条件的物品组合,最后选择最优者,这很像我们以前看过的一个算法,这不是贪婪算法。
-
And maybe you give it a set of constraints.
你可能给了它一系列的约束条件。
-
The constraint has to change. I've added a constraint.
必须改变约束条件。
-
Possibly an empty set of constraints.
约束条件得出的也可能是空值。
-
These other two, primaries and higher dimensions, they're both great points.
另外两个约束条件,初选和多维度,这二者都是很有现实意义的
-
I've simply added this one extra constraint, nice thing about thinking about it this way is it's easy to think about it, and what do you think I'll have to do if I want to go change the code?
我已经加上这个额外的约束条件，思考这种方法有件好事,那就是以这种方式来思考很简答,你认为我要是想？
-
STUDENT: The constraints.
学生：约束条件。
-
What about the one about many candidates?
那么多个候选人的约束条件呢
-
So you start with your basic model, then you add in, you enrich the model, and you see if the results change, and that'll help you explain why you're getting different results in different settings.
你们从最原始的模型开始,加入约束条件来丰富这个模型,然后检验结果是否有变,这能帮助我们解释,为什么在不同条件下结果是不同的
应用推荐