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Termination Conditions
Termination conditions are the prerequisite factors for ending the function of optimization algorithms. When a termination condition value is met, the optimization algorithm ceases functioning.
Levenberg-Marquardt, Gradient, Quasi-Newton, Minimax, Gradient Minimax, Least Pth Algorithms
These algorithms cease functioning when one of the following termination conditions are met:
RMS error
When the RMS deviation between all of the simulated and measured data points is less than the value specified in the optimizer Options table.
Maximum error
When the absolute maximum error between the simulated and measured data is less than the value specified in the Options table.
Parameter tolerance
When the percent change in each parameter value from one iteration to the next is less than the value specified in the Options table.
Function tolerance
When the percent change in the RMS error from one iteration to the next is less than the value specified in the Options table.
Maximum function evaluations
When the number of function evaluations exceeds the value specified in the Options table. This number specifies the number of times the setup is simulated. This, in turn, specifies the number of optimizer iterations.
Maximum iterations
When the number of iterations meets the value specified in the Options table.
Random, Hybrid (Random/LM), Hybrid (Random/Quasi-Newton), and Genetic Algorithms
The random, hybrids, and genetic algorithms cease functioning when one of the following termination conditions are met:
RMS error
When the RMS deviation between all of the simulated and measured data points is less than the value specified in the optimizer Options table.
Random iterations
When the number of random optimizer iterations meets the number specified on the Options table.
Maximum function evaluations
When the number of function evaluations exceeds the value specified in the Options table. This number specifies the number of times the setup is simulated. This, in turn, specifies the number of optimizer iterations.