I can not understand this problem

The SVD algorithm can be used to solve the Least Square problem introduced in the class.

-Research the solution and briefly describe the algorithm used and the intuition behind using SVD .

-Implement the described algorithm using your own Matlab or Python code [ you can use the MATLAB SVD built-in function]

-Apply the algorithm for the following linear regression problem:

oGenerate a data set consisting of ``samples'' of each of four variables using the following Matlab code:

N=25;

d1=rand(N,1);

d2=rand(N,1);

d3=rand(N,1);

d4=4*d1-3*d2+2*d3-1;

oIntroduce small ``errors'' into the data

error_var=1.e-5;

d1=d1.*(1+ error_var *rand(N,1));

d2=d2.*(1+ error_var *rand(N,1));

d3=d3.*(1+ error_var *rand(N,1));

d4=d4.*(1+ error_var *rand(N,1));

oImagine the four vectors d1, d2 , d3 ,d4as data given to you, and construct the matrix consisting of the four column vectors A=[d1, d2 , d3 ,d4], We are seeking the coefficient vector x such that

x1d1+x2d2+x3d3+x4d4=1

oUse the SVD based least square solution to find the Least square values of x.