gucci said:so does anyone know where I made a mistake? Thanks for any help!
The Matrix L1-Norm is a mathematical concept used to measure the magnitude of a matrix. It is also known as the maximum absolute column sum or the Taxicab norm. It is calculated by taking the sum of the absolute values of each column in a matrix and then selecting the maximum value from those sums.
The Matrix L1-Norm is useful in many fields, including statistics, machine learning, and signal processing. It can be used to compare the size of different matrices, identify outliers, and determine the most important features in a dataset.
In the context of Matrix L1-Norm, "Max of Column Sums" refers to the process of finding the maximum value after taking the sum of the absolute values of each column in a matrix. This value represents the Matrix L1-Norm of the matrix.
Matrix L1-Norm is different from other matrix norms, such as the L2-Norm or the Frobenius norm, because it only considers the absolute values of the elements in a matrix. This makes it more robust to outliers and can provide a more accurate measure of the magnitude of a matrix.
Yes, the Matrix L1-Norm can be used for non-square matrices. It can be applied to any matrix with any number of rows and columns as long as the values are real numbers. However, it is important to note that the Matrix L1-Norm may not always be defined for non-square matrices as it depends on the dimensions of the matrix and the values of its elements.