hello
for sparse recovery using lasso , Landa should be chosen like this or arbitrary ?
minimize norm( Ax - b ,2) + landa* norm( x,1)
lambda_max = norm( A’b, ‘inf’ );
lambda = 0.1lambda_max;
what about this? in this mode how should I choose error in term of A and y?
minimize norm(x,1)
s.t
norm(y-Ax)<error
Thanks

Namely A x - b \sim \mathcal{N} \left( 0, {{\sigma}_{n}}^{2} \right) and x \sim \mathcal{L} \left( 0, b \right) .
Now, the parameter \lambda can be derived from {\sigma}_{n} and b of your model.

Optimization View

See the above as the some kind of Lagrangian of minimization problem of the {\ell}_{1} Norm of x under constraints of the Linear System equality (I’m not accurate here as the Lagrangian should be in Linear form but the idea…).