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here weamp;#39;ll solve a least square approximation problem using the QR decomposition so recall that QR decomposition is when a is equal to Q R and to solve least square problem you need to find D which is equal to Q transpose P where P is the set of data that is given to you at the beginning so you already know the values and then you will solve are 0 x equals to 0 where X are the values you are looking for are zeros is the upper triangular matrix but only with rows that contain nonzero elements on with only rows that have at least one nonzero elements if if I have some rows with one loose button there is one row or maybe two with only zeros you would cut that row and so our zero would be given here in this case yeah it will only contain rows that have at least one nonzero element and the PI D 0 basically remember that T is equal to t0 t1 this you know is if by are you cut the last row then by T you will also cut the the last row but the goal is to have the same number as row rows