使用Eigen求解密集的，受约束的最小二乘拟合 [英] Using Eigen to solve a dense, constrained least squares fit

问题描述

对于大小为4的向量x，我需要解决形式为Ax = b的经典问题.A约为500个数据点，因此是一个密集的500x4矩阵.

I need to solve a classic problem of the form Ax = b for a vector x that is of size 4. A is on the order of ~500 data points and thus is a dense 500x4 matrix.

目前，我可以使用此处中所述的普通方程式来解决此问题，并且工作正常但是我想将x中的参数之一限制为永远不超过特定值.

Currently I can solve this using the normal equations described here and it works fine however I would like to constrain one of my parameters in x to never be above a certain value.

是否可以使用Eigen以编程方式执行此操作?

Is there a good way to do this programmatically with Eigen?

推荐答案

您可以尝试基于Eigen 有.您仍然必须形成法线方程.

You can try my quadradic programming solver based on Eigen there. You'll still have to form the normal equation.

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