[Scipysvn] r6226  trunk/scipy/optimize
scipysvn@scip...
scipysvn@scip...
Wed Feb 10 01:42:25 CST 2010
Author: stefan
Date: 20100210 01:42:25 0600 (Wed, 10 Feb 2010)
New Revision: 6226
Modified:
trunk/scipy/optimize/tnc.py
Log:
DOC: Reformat TNC docstring.
Modified: trunk/scipy/optimize/tnc.py
===================================================================
 trunk/scipy/optimize/tnc.py 20100210 07:41:40 UTC (rev 6225)
+++ trunk/scipy/optimize/tnc.py 20100210 07:42:25 UTC (rev 6226)
@@ 86,106 +86,86 @@
"""Minimize a function with variables subject to bounds, using
gradient information.
 :Parameters:
 func : callable func(x, *args)
 Function to minimize. Should return f and g, where f is
 the value of the function and g its gradient (a list of
 floats). If the function returns None, the minimization
 is aborted.
 x0 : list of floats
 Initial estimate of minimum.
 fprime : callable fprime(x, *args)
 Gradient of func. If None, then func must return the
 function value and the gradient (f,g = func(x, *args)).
 args : tuple
 Arguments to pass to function.
 approx_grad : bool
 If true, approximate the gradient numerically.
 bounds : list
 (min, max) pairs for each element in x, defining the
 bounds on that parameter. Use None or +/inf for one of
 min or max when there is no bound in that direction.
 scale : list of floats
 Scaling factors to apply to each variable. If None, the
 factors are uplow for interval bounded variables and
 1+x] fo the others. Defaults to None
 offset : float
 Value to substract from each variable. If None, the
 offsets are (up+low)/2 for interval bounded variables
 and x for the others.
 messages :
 Bit mask used to select messages display during
 minimization values defined in the MSGS dict. Defaults to
 MGS_ALL.
 maxCGit : int
 Maximum number of hessian*vector evaluations per main
 iteration. If maxCGit == 0, the direction chosen is
 gradient if maxCGit < 0, maxCGit is set to
 max(1,min(50,n/2)). Defaults to 1.
 maxfun : int
 Maximum number of function evaluation. if None, maxfun is
 set to max(100, 10*len(x0)). Defaults to None.
 eta : float
 Severity of the line search. if < 0 or > 1, set to 0.25.
 Defaults to 1.
 stepmx : float
 Maximum step for the line search. May be increased during
 call. If too small, it will be set to 10.0. Defaults to 0.
 accuracy : float
 Relative precision for finite difference calculations. If
 <= machine_precision, set to sqrt(machine_precision).
 Defaults to 0.
 fmin : float
 Minimum function value estimate. Defaults to 0.
 ftol : float
 Precision goal for the value of f in the stoping criterion.
 If ftol < 0.0, ftol is set to 0.0 defaults to 1.
 xtol : float
 Precision goal for the value of x in the stopping
 criterion (after applying x scaling factors). If xtol <
 0.0, xtol is set to sqrt(machine_precision). Defaults to
 1.
 pgtol : float
 Precision goal for the value of the projected gradient in
 the stopping criterion (after applying x scaling factors).
 If pgtol < 0.0, pgtol is set to 1e2 * sqrt(accuracy).
 Setting it to 0.0 is not recommended. Defaults to 1.
 rescale : float
 Scaling factor (in log10) used to trigger f value
 rescaling. If 0, rescale at each iteration. If a large
 value, never rescale. If < 0, rescale is set to 1.3.
+ Parameters
+ 
+ func : callable func(x, *args)
+ Function to minimize. Should return f and g, where f is
+ the value of the function and g its gradient (a list of
+ floats). If the function returns None, the minimization
+ is aborted.
+ x0 : list of floats
+ Initial estimate of minimum.
+ fprime : callable fprime(x, *args)
+ Gradient of func. If None, then func must return the
+ function value and the gradient (f,g = func(x, *args)).
+ args : tuple
+ Arguments to pass to function.
+ approx_grad : bool
+ If true, approximate the gradient numerically.
+ bounds : list
+ (min, max) pairs for each element in x, defining the
+ bounds on that parameter. Use None or +/inf for one of
+ min or max when there is no bound in that direction.
+ scale : list of floats
+ Scaling factors to apply to each variable. If None, the
+ factors are uplow for interval bounded variables and
+ 1+x] fo the others. Defaults to None
+ offset : float
+ Value to substract from each variable. If None, the
+ offsets are (up+low)/2 for interval bounded variables
+ and x for the others.
+ messages :
+ Bit mask used to select messages display during
+ minimization values defined in the MSGS dict. Defaults to
+ MGS_ALL.
+ maxCGit : int
+ Maximum number of hessian*vector evaluations per main
+ iteration. If maxCGit == 0, the direction chosen is
+ gradient if maxCGit < 0, maxCGit is set to
+ max(1,min(50,n/2)). Defaults to 1.
+ maxfun : int
+ Maximum number of function evaluation. if None, maxfun is
+ set to max(100, 10*len(x0)). Defaults to None.
+ eta : float
+ Severity of the line search. if < 0 or > 1, set to 0.25.
+ Defaults to 1.
+ stepmx : float
+ Maximum step for the line search. May be increased during
+ call. If too small, it will be set to 10.0. Defaults to 0.
+ accuracy : float
+ Relative precision for finite difference calculations. If
+ <= machine_precision, set to sqrt(machine_precision).
+ Defaults to 0.
+ fmin : float
+ Minimum function value estimate. Defaults to 0.
+ ftol : float
+ Precision goal for the value of f in the stoping criterion.
+ If ftol < 0.0, ftol is set to 0.0 defaults to 1.
+ xtol : float
+ Precision goal for the value of x in the stopping
+ criterion (after applying x scaling factors). If xtol <
+ 0.0, xtol is set to sqrt(machine_precision). Defaults to
+ 1.
+ pgtol : float
+ Precision goal for the value of the projected gradient in
+ the stopping criterion (after applying x scaling factors).
+ If pgtol < 0.0, pgtol is set to 1e2 * sqrt(accuracy).
+ Setting it to 0.0 is not recommended. Defaults to 1.
+ rescale : float
+ Scaling factor (in log10) used to trigger f value
+ rescaling. If 0, rescale at each iteration. If a large
+ value, never rescale. If < 0, rescale is set to 1.3.
 :Returns:
 x : list of floats
 The solution.
 nfeval : int
 The number of function evaluations.
 rc :
 Return code as defined in the RCSTRINGS dict.
+ Returns
+ 
+ x : list of floats
+ The solution.
+ nfeval : int
+ The number of function evaluations.
+ rc :
+ Return code as defined in the RCSTRINGS dict.
 :SeeAlso:
  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 : ndimensional rootfinding

  brentq, brenth, ridder, bisect, newton : onedimensional rootfinding

  fixed_point : scalar fixedpoint finder

  OpenOpt : a tool which offers a unified syntax to call this and
 other solvers with possibility of automatic differentiation.

"""
+ """
x0 = asarray(x0, dtype=float).tolist()
n = len(x0)
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