Otherwise, continue as follows: The definition of 'distance' is the minimum distance between any two points A,B on the two lines. So assume points A,B are the ones who provide the minimum distance between the lines.
There's a few nice ways to do this but I focus on the technique of (1) make the minimum eigenvalue 0, i.e. all associated eigenvectors for the minimum eigenvalue $\in \ker A$.
How do you compute the minimum of two independent random variables in the general case ? In the particular case there would be two uniform variables with a difference support, how should one proceed ?
Now that got me thinking that what would be the minimum number of numbers in a sudoku grid such that it can be solved. Following are the rules of Sudoku and the grid is as follows: A $9×9$ square must be filled in with numbers from $1-9$ with no repeated numbers in each line, horizontally or vertically.
Here is an extract: "This method and namely its computer implementation was developed by W.H.F. Smith and P.Wessel in1990. The interpolated surface by the Minimum Curvature method is analogous to a thin, linearly elastic plate passing through each of the data values with a minimum amount of bending.
The method you used of making the rankings the same or reversed will indeed produce the maximum and minimum values for the covariance and the correlation coefficient.
