相关机器学习技术来解决优化问题
考虑某个维度 n 的优化问题，给定一些线性方程组（不等式）或对形成凸区域的输入的约束，找到某个表达式的最大\最小值，该表达式是输入（或维度）的某种线性组合。
对于较大的维度，这些优化问题需要花费很多时间才能给出准确的答案。
那么，我们能否使用机器学习技术，在更短的时间内得到一些近似解。
如果我们可以在这种情况下使用机器学习技术，训练集应该如何？
Consider an optimization problem of some dimension n, Given some linear set of equations(inequalities) or constraints on the inputs which form a convex region, finding the maximum\minimum value of some expression which is some linear combination of inputs(or dimensions).
For larger dimension, these optimization problems take much time to give the exact answer.
So, can we use machine learning techniques, to get some approximate solution in lesser time.
if we can use machine learning techniques in this context, How the Training set should be??
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您的意思是“训练集应该有多大？”如果是这样，那么这很大程度上就是一个“一根绳子有多长”的问题。它需要足够大以适合所使用的算法，并表示正在建模的数据。
Do you mean "How big should the training set be?" If so, then that is very much a "how long is a piece of string" question. It needs to be large enough for the algorithm being used, and to represent the data that is being modeled.
在我看来，这并不特别关注机器学习，正如该术语通常所指的那样。这只是一个简单的约束优化问题。你说现在找到解决方案需要太长时间，但你没有提到你将如何尝试解决问题。
单纯形算法是为此类问题设计的，但在最坏的情况下它是指数的。你正在尝试的事情是否花费了太长时间？如果是这样，那么有大量的元启发法可能会表现良好。禁忌搜索、模拟退火、进化算法、可变深度搜索，甚至是简单的多起点爬山算法。在尝试任何异国情调之前，我可能会尝试一些类似的东西。
This doesn't strike me as being especially focused on machine learning, as is typically meant by the term anyway. It's just a straightforward constrained optimization problem. You say that it takes too long to find solutions now, but you don't mention how you're trying to solve the problem.
The simplex algorithm is designed for this sort of problem, but it's exponential in the worst case. Is that what you're trying that's taking too long? If so, there are tons of metaheuristics that might perform well. Tabu search, simulated annealing, evolutionary algorithms, variable depth search, even simple multistart hill climbers. I would probably try something along those lines before I tried anything exotic.