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Have you tried the preconditioner diag(A'*A)+lmb*speye(size(A))? For lmb large enough, I would imagine this preconditioner would accelerate the convergence significantly. diag(A'*A) can be computed fairly efficiently using $B(i,i) = \sum_j A^T(i,j)A(j,i) = \sum_j A(j,i)A(j,i)$. Another idea is to use fixed point iterations as preconditioners. Since your ...


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The nonlinear conjugate gradient method will converge for a quadratic function in $N$ steps at most. You do not have a quadratic function, and have no guarantees on convergence. It can be helpful to reset your search direction every $N$ steps or to use a better method (i.e., Newton's method). A simple function that you could use to test would be a quadratic ...


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