NLPLM Call

Language Reference

NLPLM Call

calculates Levenberg-Marquardt least squares

CALL NLPLM( rc, xr, "fun", x0, opt, blc, tc, par, "ptit", "jac">);

See "Nonlinear Optimization and Related Subroutines" for a listing of all NLP subroutines. See "Nonlinear Optimization Examples" for a description of the inputs to and outputs of all NLP subroutines.

The NLPLM subroutine uses the Levenberg-Marquardt method, which is an efficient modification of the trust-region method for nonlinear least-squares problems and is implemented as in Mor $\acute{e}$ (1978). This is the recommended algorithm for small to medium least-squares problems. Large least-squares problems can often be processed more efficiently with other subroutines, such as the NLPCG and NLPQN methods. In each iteration, the NLPLM subroutine solves a quadratically-constrained quadratic minimization problem that restricts the step to the boundary or interior of an n-dimensional elliptical trust region.

The m functions f₁(x), ... ,f_m(x) are computed by the module specified with the "fun" module argument. The m×n Jacobian matrix, J, contains the first-order derivatives of the m functions with respect to the n parameters, as follows:

$J(x) = (\nabla f_1, ... ,\nabla f_m) = ( \frac{\partial f_i}{\partial x_j} )$

You can specify J with the "jac" module argument; otherwise, the subroutine will compute it with finite difference approximations. In each iteration, the subroutine computes the crossproduct of the Jacobian matrix, J^T J, to be used as an approximate Hessian.

Note: In least-squares subroutines, you must set the first element of the opt vector to m, the number of functions.

In addition to the standard iteration history, the NLPLM subroutine also prints the following information:

Under the heading Iter, an asterisk (*) printed after the iteration number indicates that the computed Hessian approximation was singular and had to be ridged with a positive value.
The heading lambda represents the Lagrange multiplier, $\lambda$ . This has a value of zero when the optimum of the quadratic function approximation is inside the trust region, in which case a trust-region-scaled Newton step is performed. It is greater than zero when the optimum is at the boundary of the trust region, in which case the scaled Newton step is too long to fit in the trust region and a quadratically-constrained optimization is done. Large values indicate optimization difficulties, and as in Gay (1983), a negative value indicates the special case of an indefinite Hessian matrix.
The heading rho refers to $\rho$ , the ratio between the achieved and predicted difference in function values. Values that are much smaller than one indicate optimization difficulties. Values close to or larger than one indicate that the trust region radius can be increased.

Figure 17.5 shows the iteration history for the solution of the unconstrained Rosenbrock problem. See the section, "Unconstrained Rosenbrock Function", for the statements that generate this output.

                           Optimization Start
                           Parameter Estimates
        

                                             Gradient
                                             Objective
         N Parameter         Estimate        Function

         1 X1               -1.200000     -107.799999
         2 X2                1.000000      -44.000000

                 Value of Objective Function = 12.1


                 Levenberg-Marquardt Optimization

              Scaling Update of More (1978)
              Gradient Computed by Finite Differences
              CRP Jacobian Computed by Finite Differences

                Parameter Estimates                    2
                Functions (Observations)               2

                          Optimization Start

Active Constraints                  0  Objective Function           12.1
Max Abs Gradient Element  107.7999987  Radius               2626.5613171



                                                                                                  
                                                                                                                                                                                                Actual
                                                             
                     Function        Active       Objective    
  Iter    Restarts      Calls   Constraints        Function      

     1           0          4             0         2.18185      
     2           0          6             0         1.59370      
     3           0          7             0         1.32848     
     4           0          8             0         1.03891      
     5           0          9             0         0.78943   
     6           0         10             0         0.58838      
     7           0         11             0         0.34224    
     8           0         12             0         0.19630      
     9           0         13             0         0.11626     
    10           0         14             0       0.0000396     
    11           0         15             0      2.4652E-30   

                                             Ratio
                                           Between
                                            Actual
           Objective    Max Abs                and
           Function   Gradient           Predicted
  Iter     Change    Element    Lambda      Change

     1     9.9181    17.4704   0.00804       0.964
     2     0.5881     3.7015    0.0190       0.988
     3     0.2652     7.0843   0.00830       0.678
     4     0.2896     6.3092   0.00753       0.593
     5     0.2495     7.2617   0.00634       0.486
     6     0.2011     7.8837   0.00462       0.393
     7     0.2461     6.6815   0.00307       0.524
     8     0.1459     8.3857   0.00147       0.469
     9     0.0800     9.3086   0.00016       0.409
    10     0.1162     0.1781         0       1.000
    11   0.000040   4.44E-14         0       1.000

                    Optimization Results

Iterations                   11  Function Calls                      16
Jacobian Calls               12  Active Constraints                   0
Objective Function  2.46519E-30  Max Abs Gradient Element  4.440892E-14
Lambda                        0  Actual Over Pred Change              1
Radius             0.0178062912

   ABSGCONV convergence criterion satisfied.


                           Optimization Results
                           Parameter Estimates


                                                  Gradient
                                                  Objective
              N Parameter         Estimate        Function

              1 X1                1.000000    -4.44089E-14
              2 X2                1.000000    2.220446E-14

             Value of Objective Function = 2.46519E-30

Figure 17.5: Iteration History for the NLPLM Subroutine

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