任意约束的R优化 [英] Optimization in R with arbitrary constraints

问题描述

我在Excel中做过，但需要在R中运行一个适当的模拟。

I have done it in Excel but need to run a proper simulation in R.

我需要最小化函数 F / code>（ x 是向量），同时具有 sum（x）= 1 x 中的值是 [0,1] ，另一个函数 G（x） G_0 。

I need to minimize function F(x) (x is a vector) while having constraints that sum(x)=1, all values in x are [0,1] and another function G(x) > G_0.

我尝试过 optim 和 constrOptim 。他们没有给你这个选择。

I have tried it with optim and constrOptim. None of them give you this option.

推荐答案

您所指的问题是（假设）使用非线性约束的非线性优化。这是最一般的优化问题之一。

The problem you are referring to is (presumably) a non-linear optimization with non-linear constraints. This is one of the most general optimization problems.

我用于这些目的的包名为 nloptr ：请参阅这里。从我的经验，它是多才多艺，快速。您可以通过相应地设置 eval_g_eq 和 eval_g_ineq 来指定等于和不等于constaints。如果明确地知道雅可比（可以从分析得出），则指定它们用于更快的收敛;

The package I have used for these purposes is called nloptr: see here. From my experience, it is both versatile and fast. You can specify both equality and inequality constaints by setting eval_g_eq and eval_g_ineq, correspondingly. If the jacobians are known explicitly (can be derived analytically), specify them for faster convergence; otherwise, a numerical approximation is used.

使用此列表作为优化问题的一般参考。

Use this list as a general reference to optimization problems.

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