[Linear Algebra] Least Squares Problem

Jason Lee·2022년 9월 18일

Linear Algebra

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Least Squares Problem

  • Sum of squared errors : ∥b−Ax∥\begin{Vmatrix} \textbf{b} - A\textbf{x} \end{Vmatrix}
  • Definition : given an overdetermined system Ax≃bA\textbf{x} \simeq \textbf{b} where A∈Rm×n,x∈Rm,b∈RnA \in \mathbb{R}^{m \times n}, \textbf{x} \in \mathbb{R}^{m}, \textbf{b} \in \mathbb{R}^{n} and m≫nm \gg n, a lest squares solution x^\hat{\textbf{x}} is defined as x^=argminx∥b−Ax∥\hat{\textbf{x}} = \underset{\textbf{x}}{\textrm{argmin}} \begin{Vmatrix} \textbf{b} - A\textbf{x} \end{Vmatrix}
  • The most important aspect of the least-squares problem is that no matter what x\textbf{x} we select, the vector AxA \textbf{x} will necessarily be in the column space ColA\textrm{Col} A
  • Thus, we seek for x\textbf{x} that makes AxA \textbf{x} as the closest point in ColA\textrm{Col} A to b\textbf{b}
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