The normal equations arise in several branches of mathematics, from statistics to geometry. In this article, we discuss how they emerge and how to solve them.
Emergence of the normal equations
- The normal equations define the orthogonal projection of a vector onto a linear subspace.
- They equivalently define the vector that minimizes the distance between a vector and a linear subspace (see: the Moore-Penrose solution).
- They arise while solving the linear least square regression.
- They equivalently arise while solving the maximum likehood estimator for a linear regression with Gaussian error.
The normal equations
Let be a matrix and a vector. The normal equations are writen in matrix form as follow:
As stated in the previous section, a unique solution to these equations is at the same time:
Solving the normal equations
When the matrix has rank , it is invertible and the normal equations admit a unique solution expessed using the Moore-Penrose inverse of :
When has rank < , the normal equations form an underdetermined system and several solutions exists. As discussed in the article about the Moore-Penrose inverse, we can use an optimization algorithm such as gradient descent or stochastic gradient descent to find one numerically, or remove some columns from to reduce .