Inheritance diagram for NL2SOLLeastSq:
Public Member Functions | |
| NL2SOLLeastSq (Model &model) | |
| standard constructor | |
| NL2SOLLeastSq (NoDBBaseConstructor, Model &model) | |
| alternate constructor | |
| ~NL2SOLLeastSq () | |
| destructor | |
| void | minimize_residuals () |
| for the least squares branch. | |
Static Private Member Functions | |
| static void | calcr (int *np, int *pp, Real *x, int *nfp, Real *r, int *ui, void *ur, Vf vf) |
| evaluator function for residual vector | |
| static void | calcj (int *np, int *pp, Real *x, int *nfp, Real *J, int *ui, void *ur, Vf vf) |
| evaluator function for residual Jacobian | |
Private Attributes | |
| int | auxprt |
| auxilary printing bits (see Dakota Ref Manual): sum of 1 = x0prt (print initial guess) 2 = solprt (print final solution) 4 = statpr (print solution statistics) 8 = parprt (print nondefault parameters) 16 = dradpr (print bound constraint drops/adds) debug/verbose/normal use default = 31 (everything), quiet uses 3, silent uses 0. | |
| int | outlev |
| frequency of output summary lines in number of iterations (debug/verbose/normal/quiet use default = 1, silent uses 0) | |
| Real | dltfdj |
finite-diff step size for computing Jacobian approximation (fd_gradient_step_size) | |
| Real | delta0 |
finite-diff step size for gradient differences for H (a component of some covariance approximations, if desired) (fd_hessian_step_size) | |
| Real | dltfdc |
finite-diff step size for function differences for H (fd_hessian_step_size) | |
| int | mxfcal |
function-evaluation limit (max_function_evaluations) | |
| int | mxiter |
iteration limit (max_iterations) | |
| Real | rfctol |
relative fn convergence tolerance (convergence_tolerance) | |
| Real | afctol |
absolute fn convergence tolerance (absolute_conv_tol) | |
| Real | xctol |
x-convergence tolerance (x_conv_tol) | |
| Real | sctol |
singular convergence tolerance (singular_conv_tol) | |
| Real | lmaxs |
radius for singular-convergence test (singular_radius) | |
| Real | xftol |
false-convergence tolerance (false_conv_tol) | |
| int | covreq |
kind of covariance required (covariance): 1 or -1 ==> sigma^2 H^-1 J^T J H^-1 2 or -2 ==> sigma^2 H^-1 3 or -3 ==> sigma^2 (J^T J)^-1 1 or 2 ==> use gradient diffs to estimate H -1 or -2 ==> use function diffs to estimate H default = 0 (no covariance) | |
| int | rdreq |
whether to compute the regression diagnostic vector (regression_diagnostics) | |
| Real | fprec |
expected response function precision (function_precision) | |
| Real | lmax0 |
initial trust-region radius (initial_trust_radius) | |
Static Private Attributes | |
| static NL2SOLLeastSq * | nl2solInstance |
| evaluator functions | |
The NL2SOLLeastSq class provides a wrapper for NL2SOL (TOMS Algorithm 573), in the updated form of Port Library routines dn[fg][b ] from Bell Labs; see http://www.netlib.org/port/readme. The Fortran from Port has been turned into C by f2c. NL2SOL uses a function pointer approach for which passed functions must be either global functions or static member functions.
1.5.1