| 控制选项 |
| Nonlinear constraints |
User defined procedures to create custom nonlinear equality and/or inequality constraints on the parameters. |
| Linear constraints |
Linear equality and/or inequality constraints on the parameters. |
| Parameter bounds |
Simple parameter bounds of the type: lower_bd ≤ x_i ≤ upper_bd |
| Descent algorithms |
BFGS, DFP, Newton and BHHH. |
| Algorithm switching |
Specify descent algorithms to switch between based upon the number of elapsed iterations, a minimum change in the objective function or line search step size. |
| Weights |
Observation weights. |
| Covariance matrix type |
Compute a ML covariance matrix, a QML covariance matrix, or none. |
| Alpha |
Probability level for statistical tests. |
| Line search method |
STEPBT (quadratic and cubic curve fit), Brent’s method, BHHHStep, half-step or Strong Wolfe’s Conditions. |
| Trust region |
Activate or inactivate the trust region method and set the trust region size. |
| Active parameters |
Control which parameters are active (to be estimated) and which should be fixed to their start value. |
| Gradient Method |
Either compute an analytical gradient, or have CMLMT compute a numerical gradient using the forward, central or backwards difference method. |
| Jacobian of the constraints |
Specify procedures to compute either the Jacobian of the equality or inequality constraints. |
| Hessian Method |
Either compute an analytical Hessian, or have CMLMT compute a numerical Hessian using the forward, central or backwards difference method. |
| Gradient check |
Compares the analytical gradient computed by the user supplied function with the numerical gradient to check the analytical gradient for correctness. |
| Random seed |
Starting seed value used by the random line search method to allow for repeatable code. |
| Print output |
Controls whether (or how often) iteration output is printed and whether a final report is printed. |
| Gradient step |
Advanced feature: Controls the increment size for computing the step size for numerical first and second derivatives. |
| Random search radius |
The radius of the random search if attempted. |
| Maximum iterations |
Maximum iterations to converge. |
| Maximum elapsed time |
Maximum number of minutes to converge. |
| Maximum random search attempts |
Maximum allowed number of random line search attempts. |
| Convergence tolerance |
Convergence is achieved when the direction vector changes less than this amount. |
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