[SciPy-dev] Updated generic optimizers proposal
dmitrey
openopt@ukr....
Wed Apr 25 12:25:18 CDT 2007
Check for the case:
objFun(x) = sum(x)
x0 = [0 0 0 0 0 0 0 0 0 1] (or very small numbers instead of zeros,
smaller than typicalDeltaX/50 for example)
lb = zeros
ub = ones (or any other)
so if you use random shift for all coords (x = x_prev + deltaX, all
coords of deltaX are random), the probability of "move" is 2^(-9)=1/512
and probability of "stay" is 1-2^(-9) = 511/512.
this is valid for current update_guess() from anneal
class fast_sa:
def update_guess(self, x0):
x0 = asarray(x0)
u = squeeze(random.uniform(0.0, 1.0, size=self.dims))
T = self.T
y = sign(u-0.5)*T*((1+1.0/T)**abs(2*u-1)-1.0)
xc = y*(self.upper - self.lower) (so xc=deltaX change ALL coords)
xnew = x0 + xc
return xnew
class cauchy_sa(base_schedule):
def update_guess(self, x0):
x0 = asarray(x0)
numbers = squeeze(random.uniform(-pi/2, pi/2, size=self.dims))
xc = self.learn_rate * self.T * tan(numbers)
xnew = x0 + xc (ALSO modify ALL coords)
return xnew
class boltzmann_sa(base_schedule):
def update_guess(self, x0):
std = minimum(sqrt(self.T)*ones(self.dims),
(self.upper-self.lower)/3.0/self.learn_rate)
x0 = asarray(x0)
xc = squeeze(random.normal(0, 1.0, size=self.dims))
xnew = x0 + xc*std*self.learn_rate (ALSO modify ALL coords)
return xnew
If you use random shift for 1 coord only (sequential) there can be other
problems.
WBR, D.
william ratcliff wrote:
> The 'simple' way applies only to the anneal algorithm in scipy. When
> one chooses steps in a simulated annealing algorithm, there is always
> the question of how to step from the current point. For anneal, it is
> currently done based on an upper bound and lower bound (in one
> option). However, there is nothing to prevent the searcher from
> crawling its way out of the box. When most people imagine themselves
> searching in a bounded parameter space, that is not the expected
> behavior. Now, it is possible that the optimum solution is truly
> outside of the box and that the searcher is doing the right thing.
> However, if that is not the case, then there is a problem. So, what
> is one to do? The first obvious thing to try is to say, if you reach
> the edge of a bounded parameter, stay there. However, that is not
> ideal as you get stuck and can't explore the rest of the phase space.
> So, I use the simple heuristic that if a trial move is to take you
> outside of the box, simply stay where you are. In the next cycle, try
> to move again. This will keep you in the box, and if there is truly
> a solution outside of the box, will still move you towards the walls
> and let you know that maybe you've set your bounds improperly. Now,
> there are questions of efficiency. For example, how easy is it to get
> out of corners? Should one do reflections? However, I believe that
> my rather simple heuristic will preserve detailed balance and results
> in an algorithm that has the expected behavior and is better than
> having no option ;>
>
> As for deprecation--is it really true that
> scipy.optimize.anneal is deprecated?
>
> As for issues of this global optimizer or that global optimizer, why
> not let the user decide based on their expectations of their fitting
> surface? For some truly glassy surfaces, one is forced into
> techniques like simulated annealing, parrallel tempering, genetic
> algorithms, etc. and I imagine that their relative performance is
> based strongly on the particular problem that their are trying to solve.
>
> Cheers,
> WIlliam Ratcliff
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