solve_lsq_trust_region(n, m, uf, s, V, Delta, initial_alpha=None, rtol=0.01, max_iter=10)
This function implements a method described by J. J. More and used in MINPACK, but it relies on a single SVD of Jacobian instead of series of Cholesky decompositions. Before running this function, compute: U, s, VT = svd(J, full_matrices=False)
.
Number of variables.
Number of residuals.
Computed as U.T.dot(f).
Singular values of J.
Transpose of VT.
Radius of a trust region.
Initial guess for alpha, which might be available from a previous iteration. If None, determined automatically.
Stopping tolerance for the root-finding procedure. Namely, the solution p
will satisfy abs(norm(p) - Delta) < rtol * Delta
.
Maximum allowed number of iterations for the root-finding procedure.
Found solution of a trust-region problem.
Positive value such that (J.T*J + alpha*I)*p = -J.T*f. Sometimes called Levenberg-Marquardt parameter.
Number of iterations made by root-finding procedure. Zero means that Gauss-Newton step was selected as the solution.
Solve a trust-region problem arising in least-squares minimization.
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