Yes, thats correct :-) The book link that you mention outlines the problem 
exactly as I understand it, and I interpret it in the same way as you've 
mentioned.

On Friday, August 25, 2017 at 4:30:19 PM UTC+2, Jie Cheng wrote:
>
> Dear Jean-Paul
>
> Now I understand why the heuristic method is less preferable and have a 
> better sense of the least square method in this context.
>
> Like a simple linear regression 
>> <https://en.wikipedia.org/wiki/Linear_least_squares_(mathematics)>, the 
>> least-squares method will minimise the error of your function (as given by 
>> the quadrature data) projected onto the given finite element space (be it 
>> continuous or discontinuous, low or high order, etc.).
>>
>
> Can I  express this method as following: given the scalar data lower case 
> p_j at the quadrature points, find the nodal values upper case P_i such 
> that sum(p'_j - p_j)^2 is minimum where p'_j is the values at the 
> quadrature points obtained by interpolating P_i ?
>
> I did not derive, instead learned from this link 
> <https://books.google.com/books?id=8N18ew4LmIAC&pg=PA178&lpg=PA178&dq=quadrature+point+to+nodal+point+L2+projection&source=bl&ots=r51qRznCpd&sig=ykf8Y6-dLrWHUDOvtugJsVEE98U&hl=en&sa=X&ved=0ahUKEwjtzcGhwvLVAhWq7YMKHROvA8cQ6AEINDAC#v=onepage&q=quadrature%20point%20to%20nodal%20point%20L2%20projection&f=false>,
>  
> that the least square method yields the following equation:
>
> MP = R
>
> where M is mass matrix, P is nodal values, and R is the volume integration 
> of the scalar variable p.
>
> This involves solving additional linear equation but the equation itself 
> is easy to assemble. Is my understanding right?
>
> Thank you so much
> Jie
>

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