SciPy 0.6.0 API Documentation Generated by Endo, 2007-10-17

Interface to Constrained Optimization By Linear Approximation

Functions:
fmin_coblya(func, x0, cons, args=(), consargs=None, rhobeg=1.0, rhoend=1e-4,
            iprint=1, maxfun=1000)
    Minimize a function using the Constrained Optimization BY Linear
    Approximation (COBYLA) method

Function summary

• fmin_cobyla(func, x0, cons, args = (), consargs = None, rhobeg = 1.0, rhoend = 0.0001, iprint = 1, maxfun = 1000)

Functions

• fmin_cobyla(func, x0, cons, args = (), consargs = None, rhobeg = 1.0, rhoend = 0.0001, iprint = 1, maxfun = 1000)

  Minimize a function using the Constrained Optimization BY Linear
  Approximation (COBYLA) method
  
  Arguments:
  
  func     -- function to minimize. Called as func(x, *args)
  
  x0       -- initial guess to minimum
  
  cons     -- a sequence of functions that all must be >=0 (a single function
              if only 1 constraint)
  
  args     -- extra arguments to pass to function
  
  consargs -- extra arguments to pass to constraints (default of None means
              use same extra arguments as those passed to func).
              Use () for no extra arguments.
  
  rhobeg --  reasonable initial changes to the variables
  
  rhoend --  final accuracy in the optimization (not precisely guaranteed)
  
  iprint  -- controls the frequency of output: 0 (no output),1,2,3
  
  maxfun  -- maximum number of function evaluations.
  
  
  Returns:
  
  x -- the minimum
  
  See also:
  
  fmin, fmin_powell, fmin_cg, 
        fmin_bfgs, fmin_ncg -- multivariate local optimizers
  leastsq -- nonlinear least squares minimizer
  
  fmin_l_bfgs_b, fmin_tnc, 
        fmin_cobyla -- constrained multivariate optimizers
  
  anneal, brute -- global optimizers
  
  fminbound, brent, golden, bracket -- local scalar minimizers
  
  fsolve -- n-dimenstional root-finding
  
  brentq, brenth, ridder, bisect, newton -- one-dimensional root-finding
  
  fixed_point -- scalar fixed-point finder
  
  

Imported Names