§Solve for x with an approximate, iterative method
rather than an exact matrix inversion
§Start with guess x0, compute gradient
efficiently
with an adjoint model, search for minimum
along -„, compute new „ and repeat
§Good for non-linear problems; use conjugate gradient
or BFGS approaches
§Low-rank covariance matrix built up as iterations
progress
§As with Kalman filter, transport errors can be handled as
dynamic noise