[SciPy-dev] GSoC weekly report + need discuss of ticket 285

dmitrey openopt@ukr....
Fri Jul 27 14:30:30 CDT 2007


hi all,
so 

http://projects.scipy.org/scipy/scipy/ticket/464
(optimize.fmin_powell doesn't accept a matrix input for the initial guess)
rev 3199


http://projects.scipy.org/scipy/scipy/ticket/416
(MATRIXC2F transposing the wrong way in optimize.leastsq)
I will commit my patch if nothing better will be proposed till some hours

One more change in tnc - now return x value (optim point) is numpy.array, not Python list.

Also, now it consumes x0 as numpy.array, not Python list
(so http://projects.scipy.org/scipy/scipy/ticket/384 should be closed)

About a day was spent for a bug related to fmin_ncg, but finally it turned out to be related to sparse matrices.

So the last ticket left is bracket parameters (http://projects.scipy.org/scipy/scipy/ticket/285)

Alan Isaac proposed the new format of brent (very preliminary, of course), see below
So before implementing something like that I want to hear your opinions, is this the best way or something should be implemented in other way.
As for me, I treat the benefits from these changes to be very suspicious.
Regards, D.
 
def brent2(func, args=(), brack=None, tol=1.48e-8, full_output=0, maxiter=500):
    """ copy brent docstring here
    """
    brent = Brent(func=func, args=args, tol=tol, maxiter=maxiter)
    brent.set_bracket(brack=brack)
    brent.optimize()
    return brent.get_result(full_output=full_output)

class Brent:
    #need to rethink design of __init__
    def __init__(func, args=(), tol=1.48e-8, maxiter=500):
        self.func = func
        self.args = args
        self.tol = tol
        self.maxiter = maxiter
        self._mintol = 1.0e-11
        self._cg = 0.3819660
        self.xmin = None
        self.fval = None
        self.iter = 0
        self.funcalls = 0
        #etc......
    #need to rethink design of set_bracket (new options, etc)
    def set_bracket(self, brack = None):
        self.brack = brack
    def get_bracket_info(self):
        #set up
        func = self.func
        args = self.args
        brack = self.brack
        ### BEGIN core bracket_info code ###
        ### carefully DOCUMENT any CHANGES in core ##
        if brack is None:
            xa,xb,xc,fa,fb,fc,funcalls = bracket(func, args=args)
        elif len(brack) == 2:
            xa,xb,xc,fa,fb,fc,funcalls = bracket(func, xa=brack[0], xb=brack[1], args=args)
        elif len(brack) == 3:
            xa,xb,xc = brack
            if (xa > xc):  # swap so xa < xc can be assumed
                dum = xa; xa=xc; xc=dum
            assert ((xa < xb) and (xb < xc)), "Not a bracketing interval."
            fa = func(*((xa,)+args))
            fb = func(*((xb,)+args))
            fc = func(*((xc,)+args))
            assert ((fb<fa) and (fb < fc)), "Not a bracketing interval."
            funcalls = 3
        else:
            raise ValueError, "Bracketing interval must be length 2 or 3 sequence."
        ### END core bracket_info code ###

        return xa,xb,xc,fa,fb,fc,funcalls

    def optimize(self):
        #set up for optimization
        func = self.func
        xa,xb,xc,fa,fb,fc,funcalls = get_bracket_info(brack = self.brack, args = self.args)
        _mintol = self._mintol
        _cg = self._cg
        #################################
        #BEGIN CORE ALGORITHM
        #we are making NO CHANGES in this
        #################################
        x=w=v=xb
        fw=fv=fx=func(*((x,)+args))
        if (xa < xc):
            a = xa; b = xc
        else:
            a = xc; b = xa
        deltax= 0.0
        funcalls = 1
        iter = 0
        while (iter < maxiter):
            tol1 = tol*abs(x) + _mintol
            tol2 = 2.0*tol1
            xmid = 0.5*(a+b)
            if abs(x-xmid) < (tol2-0.5*(b-a)):  # check for convergence
                xmin=x; fval=fx
                break
            if (abs(deltax) <= tol1):
                if (x>=xmid): deltax=a-x       # do a golden section step
                else: deltax=b-x
                rat = _cg*deltax
            else:                              # do a parabolic step
                tmp1 = (x-w)*(fx-fv)
                tmp2 = (x-v)*(fx-fw)
                p = (x-v)*tmp2 - (x-w)*tmp1;
                tmp2 = 2.0*(tmp2-tmp1)
                if (tmp2 > 0.0): p = -p
                tmp2 = abs(tmp2)
                dx_temp = deltax
                deltax= rat
                # check parabolic fit
                if ((p > tmp2*(a-x)) and (p < tmp2*(b-x)) and (abs(p) < abs(0.5*tmp2*dx_temp))):
                    rat = p*1.0/tmp2        # if parabolic step is useful.
                    u = x + rat
                    if ((u-a) < tol2 or (b-u) < tol2):
                        if xmid-x >= 0: rat = tol1
                        else: rat = -tol1
                else:
                    if (x>=xmid): deltax=a-x # if it's not do a golden section step
                    else: deltax=b-x
                    rat = _cg*deltax

            if (abs(rat) < tol1):            # update by at least tol1
                if rat >= 0: u = x + tol1
                else: u = x - tol1
            else:
                u = x + rat
            fu = func(*((u,)+args))      # calculate new output value
            funcalls += 1

            if (fu > fx):                 # if it's bigger than current
                if (u<x): a=u
                else: b=u
                if (fu<=fw) or (w==x):
                    v=w; w=u; fv=fw; fw=fu
                elif (fu<=fv) or (v==x) or (v==w):
                    v=u; fv=fu
            else:
                if (u >= x): a = x
                else: b = x
                v=w; w=x; x=u
                fv=fw; fw=fx; fx=fu

            iter += 1
        #################################
        #END CORE ALGORITHM
        #################################

        self.xmin = x
        self.fval = fx
        self.iter = iter
        self.funcalls = funcalls

    def get_result(self, full_output=False):
        if full_output:
            return xmin, fval, iter, funcalls
        else:
            return xmin




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