[SciPy-user] L-BFGS in scipy

Nils Wagner nwagner at iam.uni-stuttgart.de
Thu Sep 14 01:24:00 CDT 2006


Xiaojian Wang wrote:
> Hi,
> Is anybody know which optimize module can handle general constrains?
> not like
> lower(i) < Xi  < upper(i)  in scipy.optimize.fmin_l_bfgs_b().
> instead, I would like to include constraint:
> Gi = cos(X1) + X2**2 + X3*X4 <= 0.0 
>  
> Xiaojian
>  
>  
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.


Nils

>  
>
>  
> On 9/13/06, *Nils Wagner* <nwagner at iam.uni-stuttgart.de
> <mailto:nwagner at iam.uni-stuttgart.de>> wrote:
>
>     Robert Kern wrote:
>     > Nils Wagner wrote:
>     >
>     >> Hi all,
>     >>
>     >> Has someone implemented the limited memory BFGS method in scipy ?
>     >>
>     >
>     > Yes. scipy.optimize.fmin_l_bfgs_b(). Please grep for these things.
>     >
>     >
>     Thank you Robert.
>     If bounds=None we have an unconstraint version.
>     Thus fmin_l_bfgs_b is also an unconstrained optimizer. I missed that.
>     Maybe fmin_l_bfgs_b should also be added to the list of
>     general-purpose
>     optimization routines
>
>     help (optimize) yields
>
>         A collection of general-purpose optimization routines.
>
>           fmin        --  Nelder-Mead Simplex algorithm
>                             (uses only function calls)
>           fmin_powell --  Powell's (modified) level set method (uses only
>                             function calls)
>           fmin_cg     --  Non-linear (Polak-Ribiere) conjugate gradient
>     algorithm
>                             (can use function and gradient).
>           fmin_bfgs   --  Quasi-Newton method
>     (Broydon-Fletcher-Goldfarb-Shanno);
>                             (can use function and gradient)
>           fmin_ncg    --  Line-search Newton Conjugate Gradient (can use
>                             function, gradient and Hessian).
>           leastsq     --  Minimize the sum of squares of M equations in
>                             N unknowns given a starting estimate.
>
>
>     Constrained Optimizers (multivariate)
>
>           fmin_l_bfgs_b -- Zhu, Byrd, and Nocedal's L-BFGS-B constrained
>     optimizer
>                              (if you use this please quote their papers --
>     see help)
>
>     and I disregard fmin_l_bfgs_b because it is given in the section
>     Constrained Optimizers.
>
>     Sorry for the noise.
>
>     Nils
>
>
>
>
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