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COBYLA - Constrained Optimization BY Linear Approximation

COBYLA is an implementation of Powell’s nonlinear derivative–free constrained optimization that uses a linear approximation approach. The algorithm is a sequential trust–region algorithm that employs linear approximations to the objective and constraint functions, where the approximations are formed by linear interpolation at n + 1 points in the space of the variables and tries to maintain a regular–shaped simplex over iterations. [Powell1994] [LICENSE]

class pyCOBYLA.COBYLA(pll_type=None, *args, **kwargs)

Bases: pyOpt.pyOpt_optimizer.Optimizer

COBYLA Optimizer Class - Inherited from Optimizer Abstract Class

COBYLA Optimizer Class Initialization

Keyword arguments:

  • pll_type -> STR: Parallel Implementation (None, ‘POA’-Parallel Objective Analysis), Default = None

Documentation last updated: Feb. 16, 2010 - Peter W. Jansen

__solve__(opt_problem={}, store_sol=True, disp_opts=False, store_hst=False, hot_start=False, *args, **kwargs)

Run Optimizer (Optimize Routine)

Keyword arguments:

  • opt_problem -> INST: Optimization instance
  • store_sol -> BOOL: Store solution in Optimization class flag, Default = True
  • disp_opts -> BOOL: Flag to display options in solution text, Default = False
  • store_hst -> BOOL/STR: Flag/filename to store optimization history, Default = False
  • hot_start -> BOOL/STR: Flag/filename to read optimization history, Default = False

Additional arguments and keyword arguments are passed to the objective function call.

Documentation last updated: February. 2, 2011 - Peter W. Jansen

Optimizer Options

Name Type Default Value Notes
RHOBEG float 0.5 Initial Variables Change
RHOEND float 1.0e-6 Convergence Accurancy
IPRINT int 2 Print Flag (0-None, 1-Final, 2,3-Iteration)
MAXFUN int 3500 Maximum Number of Iterations
IOUT int 6 Output Unit Number
IFILE str ‘COBYLA.out’ Output File Name