This is an implementation of the method of moving asymptotes (MMA). MMA uses a special type of convex approximation. For each step of the iterative process, a strictly convex approximating subproblem is generated and solved. The generation of these subproblems is controlled by the so-called moving asymptotes, which both stabilize and speed up the convergence of the general process. [Svanberg1987] [LICENSE]
Bases: pyOpt.pyOpt_optimizer.Optimizer
MMA Optimizer Class - Inherited from Optimizer Abstract Class
MMA 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 - Peter W. Jansen
Name | Type | Default Value | Notes |
---|---|---|---|
MAXIT | int | 1000 | Maximum Iterations |
GEPS | float | 1e-6 | Dual Objective Gradient Tolerance |
DABOBJ | float | 1e-6 | Min absolute change in the objective to indicate convergence |
DELOBJ | float | 1e-6 | Min relative change in the objective to indicate convergence |
ITRM | int | 2 | Number of consecutive iterations to indicate convergence |
IPRINT | int | 1 | Output Level (neg-None, 0-Screen, 1-File) |
IOUT | int | 6 | Output Unit Number |
IFILE | str | ‘MMA.out’ | Output File Name |