sd()

function [out, Elist]=sd(W,Nit)

Input:

• W: $\prod S \times N_s$ matrix containing initial guess for eigensolutions
• Nit: Number of iterations desired

Output:

• out: Result of Nit iterations of steepest descents
• Elist: Nit$\times$1 column vector containing result of getE() after each iteration
• DISPLAY: result of each getE() also displayed

Generalize your function in ``sd.m'' to provide the above functionality. It practice, you can accomplish this by including ``Elist'' in the function prototype at the top of the file and adding the two indicated statements to your loop as in the example below.

% Minimization with steepest descent algorithm using getE() and getgrad()
%
% Usage: [out, Elist]=sd(W,Nit)

function [out, Elist]=sd(W,Nit)
          .
          .
          .
  for it=1:Nit
    out=out-alpha*getgrad(out);
    Elist(it)=getE(out); %# <= New statements
    Elist(it)
  end
          .
          .
          .