This code reformulates the constrained problem into an unconstrained one using a composite Kreisselmeier–Steinhauser objective function to create an envelope of the objective function and set of constraints. The envelope function is then optimized using a sequential unconstrained minimization technique (SUMT). At each iteration, the unconstrained optimization problem is solved using the Davidon–Fletcher–Powell (DFP) algorithm. [Wrenn1989] [LICENSE]
Bases: pyOpt.pyOpt_optimizer.Optimizer
KSOPT Optimizer Class - Inherited from Optimizer Abstract Class
KSOPT Optimizer Class Initialization
Keyword arguments:
Documentation last updated: Feb. 16, 2010 - Peter W. Jansen
Run Optimizer (Optimize Routine)
Keyword arguments:
Additional arguments and keyword arguments are passed to the objective function call.
Documentation last updated: February. 2, 2011 - Ruben E. Perez
Name | Type | Default Value | Notes |
---|---|---|---|
ITMAX | int | 4e2 | Maximum Number of Iterations |
RDFUN | float | 1e-4 | Objective Convergence Relative Tolerance |
RHOMIN | float | 5 | Initial KS multiplier |
RHOMAX | float | 100.0 | Final KS multiplier |
IPRINT | int | 2 | Print Control (0 - None, 1 - Final, 2 - Iters) |
IOUT | int | 6 | Output Unit Number |
IFILE | str | ‘KSOPT.out’ | Output File Name |