- Hands-On Mathematics for Deep Learning
- Jay Dawani
- 282字
- 2021-06-18 18:55:09
Inverse matrices
Let's revisit the concept of inverse matrices and go a little more in depth with them. We know from earlier that AA-1 = I, but not every matrix has an inverse.
There are, again, some rules we must follow when it comes to finding the inverses of matrices, as follows:
- The inverse only exists if, through the process of upper or lower triangular factorization, we obtain all the pivot values on the diagonal.
- If the matrix is invertible, it has only one unique inverse matrix—that is, if AB = I and AC = I, then B = C.
- If A is invertible, then to solve Av = b we multiply both sides by A-1 and get AA-1v = A-1b, which finally gives us = A-1b.
- If v is nonzero and b = 0, then the matrix does not have an inverse.
- 2 x 2 matrices are invertible only if ad - bc ≠ 0, where the following applies:

And ad - bc is called the determinant of A. A-1 involves dividing each element in the matrix by the determinant.
- Lastly, if the matrix has any zero values along the diagonal, it is non-invertible.
Sometimes, we may have to invert the product of two matrices, but that is only possible when both the matrices are individually invertible (follow the rules outlined previously).
For example, let's take two matrices A and B, which are both invertible. Then, so that
.
Note: Pay close attention to the order of the inverse—it too must follow the order. The left-hand side is the mirror image of the right-hand side.
推薦閱讀
- 計算機組成原理與接口技術:基于MIPS架構實驗教程(第2版)
- 程序員修煉之道:從小工到專家
- 云數據中心基礎
- Greenplum:從大數據戰略到實現
- 輕松學大數據挖掘:算法、場景與數據產品
- Learning JavaScriptMVC
- 大數據技術入門
- 重復數據刪除技術:面向大數據管理的縮減技術
- IPython Interactive Computing and Visualization Cookbook(Second Edition)
- 機器學習:實用案例解析
- The Natural Language Processing Workshop
- 數據中臺實戰:手把手教你搭建數據中臺
- Artificial Intelligence for Big Data
- 數據會說話:活用數據表達、說服與決策
- Trino權威指南(原書第2版)