def doneMsg(self, where):
if self.code > 0:
msg('done at %s with error status: %s', where, self.errMsg)
else:
if self.params.verbose:
msg('done at %s with val=%g (%g, %g)', where, self.value.t,
self.value.F, self.value.Fp)
def ushrink(self,a,b):
abar = a;
bbar = b;
count = 0;
while True:
count += 1;
if self.params.verbose:
msg('in ushrink')
self.printBracket(abar,bbar)
if count > self.params.nshrink:
self.setError('too many contractions in ushrink')
return (abar,bbar)
d=self.eval( (1-self.params.theta)*abar.t+self.params.theta*bbar.t );
if self.wolfe(d):
self.setDone(d)
return (abar,bbar)
if d.Fp>=0:
bbar = d;
return (abar,bbar)
if d.F <= self.fpert:
abar=d;
else:
bbar=d;
def update(self, a, b, ct ):
abar = a
bbar = b
params = self.params
if params.verbose: msg('update %g %g %g', a.t, b.t, ct);
if (ct<=a.t) or (ct>=b.t):
if params.verbose: msg('midpoint out of interval')
return (abar,bbar)
c = self.eval(ct)
if self.wolfe(c):
self.setDone(c)
return (abar,bbar)
if c.Fp >= 0:
if params.verbose: msg('midpoint with non-negative slope. Becomes b.')
abar = a;
bbar = c;
if params.debug: self.verifyBracket(abar,bbar)
return (abar,bbar)
if c.F <= self.fpert:
if params.verbose: msg('midpoint with negative slope, small value. Becomes a.')
abar = c;
bbar = b;
if params.debug: self.verifyBracket(abar,bbar)
return (abar,bbar)
if params.verbose: msg('midpoint with negative slope, large value. Shrinking to left.')
(abar,bbar) = self.ushrink( a, c );
if params.debug: self.verifyBracket(abar,bbar)
def ushrink(self, a, b):
abar = a
bbar = b
count = 0
while True:
count += 1
if self.params.verbose:
msg('in ushrink')
self.printBracket(abar, bbar)
if count > self.params.nshrink:
self.setError('too many contractions in ushrink')
return (abar, bbar)
d = self.eval((1 - self.params.theta) * abar.t +
self.params.theta * bbar.t)
if self.wolfe(d):
self.setDone(d)
return (abar, bbar)
if d.Fp >= 0:
bbar = d
return (abar, bbar)
if d.F <= self.fpert:
abar = d
else:
bbar = d
def verifyBracket(self,a,b):
good = (a.Fp<=0) and (b.Fp >=0) and (a.F<= self.fpert);
if not good:
msg( 'bracket inconsistant: a %g b %g f(a) %g fp(a) %g f(b) %g fp(b) %g fpert %g', a.t, b.t, a.F, a.Fp, b.F, b.Fp, self.fpert )
pause()
if(a.t>=b.t):
msg('bracket not a bracket (a>=b): a %g b %g f(a) %g fp(a) %g f(b) %g fp(b) %g fpert %g', a.t, b.t, a.F, a.Fp, b.F, b.Fp, self.fpert )
def eval(self,x,d,y,t):
"""
Efficiently evaluate the function Phi(t) and Phi'(t) where
Phi(t) = 0.5 * || y- F(x+td) ||_Y^2
as described in the classlevel documentation.
"""
if self.z is None:
self.z = x.copy()
self.r = y.copy()
# self.Fz = dolfinutils.vectorLike(y)
self.Td = y.vector_like()
else:
self.z.set(x)
self.r.set(y)
forward_problem = self.forward_problem
self.z.axpy(t,d)
try:
self.Fz = forward_problem.evalFandLinearize(self.z,out=self.Fz,guess=self.Fz)
except Exception as e:
msg('Exception during evaluation of linesearch at t=%g;\n%s',t,str(e))
return (numpy.nan,numpy.nan,None)
self.Td=forward_problem.T(d,out=self.Td)
self.r -= self.Fz
J = 0.5*forward_problem.rangeIP(self.r,self.r)
Jp = -self.forward_problem.rangeIP(self.Td,self.r)
# Extra return value 'None' can be used to store extra data.
return (J,Jp,None)
def solve(self,x0,y,*args):
(x,y) = self.initialize(x0,y,*args)
cg_reset = x.size()
if( self.params.cg_reset != 0): cg_reset = self.params.cg_reset
forward_problem = self.forwardProblem()
r=y.copy()
Tx = forward_problem.T(x)
r -= Tx
normsq_r = forward_problem.domainIP(r,r)
# d = r
d = r.copy()
# Eventually will contain T(d)
Td = None
count = 0
while True:
if count > self.params.ITER_MAX:
raise IterationCountFailure(self.params.ITER_MAX)
count += 1
if self.stopConditionMet(count,x,y,r):
msg('done at iteration %d', count)
break
Td = forward_problem.T(d,out=Td)
self.iterationHook( count, x, y, d, r, Td )
alpha = normsq_r/forward_problem.domainIP(d,Td)
if( (count+1 % cg_reset) == 0 ):
msg('resetting cg via steepest descent')
alpha = 1
# x = x + alpha*d
x.axpy(alpha,d)
# r = r - alpha*Td
r.axpy(-alpha,Td)
prev_normsq_r = normsq_r
normsq_r = forward_problem.domainIP(r,r)
beta = normsq_r / prev_normsq_r
if( (count+1 % cg_reset) == 0 ): beta = 0
if(self.params.steepest_descent):
beta = 0
# d = T*r + beta*d
d *= beta
d += r
y = forward_problem.T(x)
return self.finalize(x,y)
def stopConditionMet(self, iter, x, y, r):
"""
Given a current iteration number, current value of x, desired value y of F(X), and current residual,
returns whether the stop condition has been met.
"""
disc = sqrt(self.forward_problem.rangeIP(r, r))
target = self.params.mu * self.targetDiscrepancy
if self.params.verbose:
msg('Iteration %d: discrepancy %g target %g', iter, disc, target)
return disc <= target
def stopConditionMet(self,iter,x,y,r):
"""
Given a current iteration number, current value of x, desired value y of F(X), and current residual,
returns whether the stop condition has been met.
"""
disc = sqrt(self.forward_problem.rangeIP(r,r))
target = self.params.mu*self.targetDiscrepancy
if self.params.verbose:
msg('Iteration %d: discrepancy %g target %g',iter,disc,target)
return disc <= target
def secant(self,a,b):
# % What if a'=b'? We'll generate a +/-Inf, which will subsequently test as being out
# % of any interval when 'update' is subsequently called. So this seems safe.
if self.params.verbose: msg( 'secant: a %g fp(a) %4.8g b %g fp(b) %4.8g',a.t,a.Fp, b.t, b.Fp)
if(a.t==b.t):
msg('a=b, inconcievable!')
if -a.Fp <= b.Fp:
return a.t-(a.t-b.t)*(a.Fp/(a.Fp-b.Fp));
else:
return b.t-(a.t-b.t)*((b.Fp)/(a.Fp-b.Fp));
def verifyBracket(self, a, b):
good = (a.Fp <= 0) and (b.Fp >= 0) and (a.F <= self.fpert)
if not good:
msg(
'bracket inconsistent: a %g b %g f(a) %g fp(a) %g f(b) %g fp(b) %g fpert %g',
a.t, b.t, a.F, a.Fp, b.F, b.Fp, self.fpert)
pause()
if (a.t >= b.t):
msg(
'bracket not a bracket (a>=b): a %g b %g f(a) %g fp(a) %g f(b) %g fp(b) %g fpert %g',
a.t, b.t, a.F, a.Fp, b.F, b.Fp, self.fpert)
def secant(self, a, b):
# % What if a'=b'? We'll generate a +/-Inf, which will subsequently test as being out
# % of any interval when 'update' is subsequently called. So this seems safe.
if self.params.verbose:
msg('secant: a %g fp(a) %4.8g b %g fp(b) %4.8g', a.t, a.Fp, b.t,
b.Fp)
if (a.t == b.t):
msg('a=b, inconcievable!')
if -a.Fp <= b.Fp:
return a.t - (a.t - b.t) * (a.Fp / (a.Fp - b.Fp))
else:
return b.t - (a.t - b.t) * ((b.Fp) / (a.Fp - b.Fp))
def secantsq(self, a, b):
ct = self.secant(a, b)
if self.params.verbose: msg('first secant to %g', ct)
(A, B) = self.update(a, b, ct)
if self.code >= 0:
return (A, B)
if B.t == ct:
ct2 = self.secant(b, B)
if self.params.verbose:
msg('second secant on left half A %g B %g with c=%g', A.t, B.t,
ct2)
(abar, bbar) = self.update(A, B, ct2)
elif A.t == ct:
ct2 = self.secant(a, A)
if self.params.verbose:
msg('second secant on right half A %g B %g with c=%g', A.t,
B.t, ct2)
(abar, bbar) = self.update(A, B, ct2)
else:
if self.params.verbose:
msg('first secant gave a shrink in update. Keeping A %g B %g',
A.t, B.t)
abar = A
bbar = B
return (abar, bbar)
def stopConditionMet(self, count, x, Fx, y, r):
"""
Determines if minimization should be halted (based, e.g. on a Morozov discrepancy principle)
In:
* count: current iteration count
* x: point in domain of potential minimizer.
* Fx: value of nonlinear function at x
* y: desired value of F(x)
* r: current residual
"""
J = sqrt(abs(self.forward_problem.rangeIP(r, r)))
Jgoal = self.params.mu * self.targetDiscrepancy
msg('(%d) J=%g goal=%g', count, J, Jgoal)
return J <= Jgoal
def stopConditionMet(self,count,x,Fx,y,r):
"""
Determines if minimization should be halted (based, e.g. on a Morozov discrepancy principle)
In:
* count: current iteration count
* x: point in domain of potential minimizer.
* Fx: value of nonlinear function at x
* y: desired value of F(x)
* r: current residual
"""
J = sqrt(abs(self.forward_problem.rangeIP(r,r)));
Jgoal = self.params.mu*self.targetDiscrepancy
msg('(%d) J=%g goal=%g',count,J,Jgoal)
return J <= Jgoal
def update(self, a, b, ct):
abar = a
bbar = b
params = self.params
if params.verbose: msg('update %g %g %g', a.t, b.t, ct)
if (ct <= a.t) or (ct >= b.t):
if params.verbose: msg('midpoint out of interval')
return (abar, bbar)
c = self.eval(ct)
if self.wolfe(c):
self.setDone(c)
return (abar, bbar)
if c.Fp >= 0:
if params.verbose:
msg('midpoint with non-negative slope. Becomes b.')
abar = a
bbar = c
if params.debug: self.verifyBracket(abar, bbar)
return (abar, bbar)
if c.F <= self.fpert:
if params.verbose:
msg('midpoint with negative slope, small value. Becomes a.')
abar = c
bbar = b
if params.debug: self.verifyBracket(abar, bbar)
return (abar, bbar)
if params.verbose:
msg('midpoint with negative slope, large value. Shrinking to left.'
)
(abar, bbar) = self.ushrink(a, c)
if params.debug: self.verifyBracket(abar, bbar)
return (abar, bbar)
def solve(self, x0, y, *args):
(x, y) = self.initialize(x0, y, *args)
cg_reset = x.size()
if (self.params.cg_reset != 0): cg_reset = self.params.cg_reset
forward_problem = self.forwardProblem()
r = y.copy()
Tx = forward_problem.T(x)
r -= Tx
normsq_r = forward_problem.domainIP(r, r)
# d = r
d = r.copy()
# Eventually will contain T(d)
Td = None
count = 0
while True:
if count > self.params.ITER_MAX:
raise IterationCountFailure(self.params.ITER_MAX)
count += 1
if self.stopConditionMet(count, x, y, r):
msg('done at iteration %d', count)
break
if self.params.verbose:
msg('solving linear problem')
Td = forward_problem.T(d, out=Td)
self.iterationHook(count, x, y, d, r, Td)
alpha = normsq_r / forward_problem.domainIP(d, Td)
if ((count + 1 % cg_reset) == 0):
msg('resetting cg via steepest descent')
alpha = 1
# x = x + alpha*d
x.axpy(alpha, d)
# r = r - alpha*Td
r.axpy(-alpha, Td)
prev_normsq_r = normsq_r
normsq_r = forward_problem.domainIP(r, r)
beta = normsq_r / prev_normsq_r
if ((count + 1 % cg_reset) == 0): beta = 0
if (self.params.steepest_descent):
beta = 0
# d = T*r + beta*d
d *= beta
d += r
y = forward_problem.T(x)
return self.finalize(x, y)
def secantsq(self,a,b):
ct = self.secant(a,b)
if self.params.verbose: msg('first secant to %g', ct)
(A,B) = self.update(a,b,ct)
if self.code >= 0:
return (A,B)
if B.t == ct:
ct2 = self.secant(b,B);
if self.params.verbose: msg('second secant on left half A %g B %g with c=%g',A.t, B.t, ct2)
(abar,bbar) = self.update(A,B,ct2)
elif A.t == ct:
ct2 = self.secant(a,A);
if self.params.verbose: msg('second secant on right half A %g B %g with c=%g',A.t, B.t, ct2)
(abar,bbar) = self.update(A,B,ct2)
else:
if self.params.verbose: msg('first secant gave a shrink in update. Keeping A %g B %g',A.t, B.t)
abar = A; bbar = B
return (abar,bbar)
def bracket(self, z, c):
a = z
b = c
count = 0
while True:
if count > self.params.nshrink:
self.setError('Too many expansions in bracket')
return (a, b)
count += 1
if b.Fp >= 0:
if self.params.verbose:
msg('initial bracket ends with expansion: b has positive slope'
)
return (a, b)
if b.F > self.fpert:
if self.params.verbose: msg('initial bracket contraction')
return self.ushrink(a, b)
if self.params.verbose: msg('initial bracket expanding')
a = b
rho = self.params.rho
while True:
if count > self.params.nshrink:
self.setError('Unable to find a valid input')
return (a, b)
c = self.eval(rho * b.t)
if not numpy.isnan(c.F):
b = c
break
msg('Hit a NaN at t=%g', rho * b.t)
rho *= 0.5
count += 1
if self.wolfe(b):
#msg('decrease %g slope %g f0 %g fpert %g', b.F-params.f0, b.t*params.wolfe_hi, params.f0, params.fpert)
self.setDone(b)
return (a, b)
def bracket( self, z, c ):
a = z
b = c
count = 0
while True:
if count > self.params.nshrink:
self.setError('Too many expansions in bracket')
return (a,b)
count += 1
if b.Fp >= 0:
if self.params.verbose: msg('initial bracket ends with expansion: b has positive slope')
return (a,b)
if b.F > self.fpert:
if self.params.verbose: msg('initial bracket contraction')
return self.ushrink(a,b);
if self.params.verbose: msg('initial bracket expanding')
a = b;
rho = self.params.rho
while True:
if count > self.params.nshrink:
self.setError('Unable to find a valid input')
return (a,b)
c = self.eval(rho*b.t)
if not numpy.isnan(c.F):
b = c
break
msg('Hit a NaN at t=%g',rho*b.t)
rho*=0.5
count += 1
if self.wolfe(b):
#msg('decrease %g slope %g f0 %g fpert %g', b.F-params.f0, b.t*params.wolfe_hi, params.f0, params.fpert)
self.setDone(b);
return(a,b)
def wolfe(self,c):
if self.params.verbose: msg('checking wolfe of c=%g (%g,%g)',c.t,c.F,c.Fp)
if c.Fp >= self.wolfe_lo:
if (c.F-self.f0) <= c.t*self.wolfe_hi:
return True
if self.params.verbose: msg('failed sufficient decrease' )
# % if ( (c.F <= params.fpert) && (c.Fp <= params.awolfe_hi ) )
# % msg('met awolfe')
# % met = true;
# % return;
# % end
# if params.verbose: msg('failed awolfe sufficient decrease' )
else:
if self.params.verbose: msg('failed slope flatness')
return False
def wolfe(self, c):
if self.params.verbose:
msg('checking wolfe of c=%g (%g,%g)', c.t, c.F, c.Fp)
if c.Fp >= self.wolfe_lo:
if (c.F - self.f0) <= c.t * self.wolfe_hi:
return True
if self.params.verbose: msg('failed sufficient decrease')
# % if ((c.F <= params.fpert) && (c.Fp <= params.awolfe_hi))
# % msg('met awolfe')
# % met = true;
# % return;
# % end
# if params.verbose: msg('failed awolfe sufficient decrease')
else:
if self.params.verbose: msg('failed slope flatness')
return False
def search(self,F,F0,F0p,t0):
self.code = -1
self.errMsg = 'no error'
self.F = F
params = self.params
z = Bunch(F=F0,Fp=F0p,t=0,data=None)
assert F0p <= 0
# % Set up constants for checking Wolfe conditions.
self.wolfe_lo = params.sigma*z.Fp;
self.wolfe_hi = params.delta*z.Fp;
self.awolfe_hi = (2*params.delta-1)*z.Fp;
self.fpert = z.F + params.epsilon;
self.f0 = z.F;
if params.verbose: msg('starting at z=%g (%g,%g)', z.t, z.F, z.Fp )
while True:
c = self.eval(t0)
if not numpy.isnan(c.F):
break
msg('Hit a NaN in initial evaluation at t=%g',t0)
t0 *= 0.5
if params.verbose: msg('initial guess c=%g (%g,%g)', c.t, c.F, c.Fp )
if self.wolfe(c):
if params.verbose: msg('done at init')
self.setDone(c)
return
(aj,bj) = self.bracket(z,c)
if params.verbose: msg('initial bracket %g %g',aj.t,bj.t)
if self.code >= 0:
self.doneMsg('initial bracket')
return
if params.debug: self.verifyBracket(aj,bj)
count = 0;
while True:
count += 1;
if count> params.nsecant:
self.setError('too many bisections in main loop')
return
(a,b) = self.secantsq(aj,bj);
if params.verbose: msg('secantsq a %g b %g', a.t, b.t)
if params.verbose: self.printBracket(a,b)
if self.code >= 0:
self.doneMsg('secant');
return
if (b.t-a.t) > params.gamma*(bj.t-aj.t):
(a,b) = self.update(a, b, (a.t+b.t)/2 );
if params.verbose: msg('update to a %g b %g', aj.t, bj.t)
if params.verbose: self.printBracket(a,b)
if self.code >= 0:
self.doneMsg( 'bisect');
return
aj = a
bj = b
def solve(self, x0, y, *args):
"""Solve the inverse problem F(x)=y. The initial estimate is x=x0.
Any extra arguments are passed to :func:`initialize`.
"""
(x,y) = self.initialize(x0,y,*args)
# self.discrepancy_history=[]
forward_problem = self.forwardProblem()
cg_reset = x.size()
if (self.params.cg_reset != 0): cg_reset = self.params.cg_reset
# Initial functional evalutation
if self.params.verbose: msg('initial evaluation')
Fx = forward_problem.evalFandLinearize(x)
residual = y.copy()
residual.axpy(-1, Fx)
Jx = 0.5*forward_problem.rangeIP(residual,residual)
TStarR = forward_problem.TStar(residual)
TStarRLast = TStarR.copy()
# Compute our first guess for the descent direction.
d = TStarR.copy();
Jp = -forward_problem.domainIP(TStarR,d)
if self.params.verbose: msg('initial J %g Jp %g', Jx, Jp)
# We need to give an initial guess for the stepsize in the linesearch routine
# t0 = Jx/(1-Jp);
t0 = Jx/(self.params.deriv_eps-Jp)
# An analog of matlab's realmin
realmin = np.finfo(np.double).tiny
# The class that performs our linesearches.
line_search = linesearchHZ.LinesearchHZ(params=self.params.linesearch)
line_searchee = ForwardProblemLineSearchAdaptor(forward_problem)
# Keep track of multiple line search failures.
prevLineSearchFailed = False
try:
# Main loop
count = 0
while True:
# self.discrepancy_history.append(sqrt(2*Jx))
if count > self.params.ITER_MAX:
raise IterationCountFailure(self.params.ITER_MAX)
count += 1
self.iterationHook(count,x,Fx,y,d,residual,TStarR)
if self.stopConditionMet(count,x,Fx,y,residual):
msg('done at iteration %d', count)
break
# Phi = lambda t: self.evalPhiAndPhiPrime(forward_problem,x,d,y,t)
Phi = lambda t: line_searchee.eval(x,d,y,t)
line_search.search(Phi,Jx,Jp,t0)
if line_search.error():
if prevLineSearchFailed:
raise NumericalFailure('linesearch failed twice in a row: %s' % line_search.errMsg);
else:
msg('linesearch failed: %s, switching to steepest descent', line_search.errMsg);
d = TStarR;
t = 1/(self.params.deriv_eps-Jp);
prevLineSearchFailed = True;
continue
prevLineSearchFailed = False;
t = line_search.value.t;
x.axpy(t,d)
TStarRLast.set(TStarR)
Fx = forward_problem.evalFandLinearize(x,out=Fx,guess=Fx)
residual.set(y)
residual -= Fx
self.xUpdateHook(count,x,Fx,y,residual)
TStarR = forward_problem.TStar(residual,out=TStarR)
Jx = 0.5*forward_problem.rangeIP(residual,residual)
if self.params.steepest_descent:
beta = 0
else:
# Polak-Ribiere
beta = forward_problem.domainIP(TStarR,TStarR-TStarRLast)/forward_problem.domainIP(TStarRLast,TStarRLast)
# If we have done more iterations than we have points, reset the conjugate gradient method.
if count > cg_reset:
beta = 0
d *= beta
d += TStarR
JpLast = Jp
Jp = -forward_problem.domainIP(TStarR,d)
if Jp >=0:
if self.params.verbose:
msg('found an uphill direction; resetting!');
d.set(TStarR)
Jp = -forward_problem.domainIP(TStarR,d);
t0 = Jx/(self.params.deriv_eps-Jp);
else:
t0 = t* min(10, JpLast/(Jp - realmin));
except Exception as e:
# Store the current x and y values in case they are interesting to the caller, then
# re-raise the exception.
import traceback
traceback.print_exc()
self.finalState = self.finalize(x,Fx)
raise e
return self.finalize(x, Fx)
def solve(self, x0, y, *args):
"""Solve the inverse problem F(x)=y. The initial estimate is x=x0.
Any extra arguments are passed to :func:`initialize`.
"""
(x, y) = self.initialize(x0, y, *args)
# self.discrepancy_history=[]
forward_problem = self.forwardProblem()
cg_reset = x.size()
if (self.params.cg_reset != 0): cg_reset = self.params.cg_reset
# Initial functional evalutation
if self.params.verbose: msg('initial evaluation')
Fx = forward_problem.evalFandLinearize(x)
residual = y.copy()
residual.axpy(-1, Fx)
Jx = 0.5 * forward_problem.rangeIP(residual, residual)
TStarR = forward_problem.TStar(residual)
TStarRLast = TStarR.copy()
# Compute our first guess for the descent direction.
d = TStarR.copy()
Jp = -forward_problem.domainIP(TStarR, d)
if self.params.verbose: msg('initial J %g Jp %g', Jx, Jp)
# We need to give an initial guess for the stepsize in the linesearch routine
# t0 = Jx/(1-Jp);
t0 = Jx / (self.params.deriv_eps - Jp)
# An analog of matlab's realmin
realmin = np.finfo(np.double).tiny
# The class that performs our linesearches.
line_search = linesearchHZ.LinesearchHZ(params=self.params.linesearch)
line_searchee = ForwardProblemLineSearchAdaptor(forward_problem)
# Keep track of multiple line search failures.
prevLineSearchFailed = False
try:
# Main loop
count = 0
while True:
# self.discrepancy_history.append(sqrt(2*Jx))
if count > self.params.ITER_MAX:
raise IterationCountFailure(self.params.ITER_MAX)
count += 1
self.iterationHook(count, x, Fx, y, d, residual, TStarR)
if self.stopConditionMet(count, x, Fx, y, residual):
msg('done at iteration %d', count)
break
# Phi = lambda t: self.evalPhiAndPhiPrime(forward_problem,x,d,y,t)
Phi = lambda t: line_searchee.eval(x, d, y, t)
line_search.search(Phi, Jx, Jp, t0)
if line_search.error():
if prevLineSearchFailed:
raise NumericalFailure(
'linesearch failed twice in a row: %s' %
line_search.errMsg)
else:
msg(
'linesearch failed: %s, switching to steepest descent',
line_search.errMsg)
d = TStarR
t = 1 / (self.params.deriv_eps - Jp)
prevLineSearchFailed = True
continue
prevLineSearchFailed = False
t = line_search.value.t
x.axpy(t, d)
TStarRLast.set(TStarR)
Fx = forward_problem.evalFandLinearize(x, out=Fx, guess=Fx)
residual.set(y)
residual -= Fx
self.xUpdateHook(count, x, Fx, y, residual)
TStarR = forward_problem.TStar(residual, out=TStarR)
Jx = 0.5 * forward_problem.rangeIP(residual, residual)
if self.params.steepest_descent:
beta = 0
else:
# Polak-Ribiere
beta = forward_problem.domainIP(
TStarR,
TStarR - TStarRLast) / forward_problem.domainIP(
TStarRLast, TStarRLast)
# If we have done more iterations than we have points, reset the conjugate gradient method.
if count > cg_reset:
beta = 0
d *= beta
d += TStarR
JpLast = Jp
Jp = -forward_problem.domainIP(TStarR, d)
if Jp >= 0:
if self.params.verbose:
msg('found an uphill direction; resetting!')
d.set(TStarR)
Jp = -forward_problem.domainIP(TStarR, d)
t0 = Jx / (self.params.deriv_eps - Jp)
else:
t0 = t * min(10, JpLast / (Jp - realmin))
except Exception as e:
# Store the current x and y values in case they are interesting to the caller, then
# re-raise the exception.
import traceback
traceback.print_exc()
self.finalState = self.finalize(x, Fx)
raise e
return self.finalize(x, Fx)
def solve(self, x0, y, *args):
(x, y) = self.initialize(x0, y, *args)
forward_problem = self.forwardProblem()
Tx = forward_problem.T(x)
r = y.copy()
r -= Tx
cg_reset = x.size()
if (self.params.cg_reset != 0): cg_rest = self.params.cg_reset
TStarR = forward_problem.TStar(r)
normsq_TStarR = forward_problem.domainIP(TStarR, TStarR)
# d = T^* r
d = TStarR.copy()
# Eventual storage for T(d)
Td = None
count = 0
while True:
if count > self.params.ITER_MAX:
raise IterationCountFailure(self.params.ITER_MAX)
break
count += 1
if self.stopConditionMet(count, x, y, r):
msg('done at iteration %d', count)
break
Td = forward_problem.T(d, out=Td)
self.iterationHook(count, x, y, d, r, Td, TStarR)
alpha = normsq_TStarR / forward_problem.rangeIP(Td, Td)
if ((count + 1 % cg_reset) == 0):
msg('resetting cg via steepest descent')
alpha = 1
# x = x + alpha*d
x.axpy(alpha, d)
# r = r - alpha*Td
r.axpy(-alpha, Td)
# beta = ||r_{k+1}||^2 / ||r_k||^2
prev_normsq_TStarR = normsq_TStarR
TStarR = forward_problem.TStar(r, out=TStarR)
normsq_TStarR = forward_problem.domainIP(TStarR, TStarR)
beta = normsq_TStarR / prev_normsq_TStarR
if ((count + 1 % cg_reset) == 0): beta = 0
if (self.params.steepest_descent):
beta = 0
# d = T*r + beta*d
d *= beta
d += TStarR
Tx = forward_problem.T(x, out=Tx)
return self.finalize(x, Tx)
def solve(self, x0, y, *args):
"""Main routine to solve the inverse problem F(x)=y. Initial guess is x=x0.
Extra arguments are passed to :func:`initialize`."""
(x,y,targetDiscrepancy) = self.initialize(x0,y,*args)
self.discrepancy_history=[]
forward_problem = self.forwardProblem()
params = self.params
cg_reset = x.size()
if (self.params.cg_reset != 0): cg_reset = self.params.cg_reset
# Initial functional evalutation
Fx = forward_problem.evalFandLinearize(x)
# Prepare some storage
Td = None
residual = y.copy()
residual.axpy(-1, Fx)
discrepancy = self.discrepancy(x,y,residual);
# The class that performs our linesearches.
line_search = linesearchHZ.LinesearchHZ(params=self.params.linesearch)
line_searchee = ForwardProblemLineSearchAdaptor(forward_problem)
# Main loop
count = 0
theta = params.thetaMax;
# try:
for kkkkk in range(1):
while True:
self.discrepancy_history.append(discrepancy)
if count > self.params.ITER_MAX:
raise IterationCountFailure(self.params.ITER_MAX)
count += 1
if theta < self.params.thetaMin:
raise NumericalFailure('Reached smallest trust region size.')
# Error to correct:
discrepancyLin = (1-theta)*discrepancy + theta*targetDiscrepancy
msg('(%d) discrepancy: current %g linear goal:%g goal: %g\n---', count, discrepancy, discrepancyLin, targetDiscrepancy)
if discrepancy <= self.params.mu*targetDiscrepancy:
msg('done at iteration %d', count)
break
# try:
d = self.linearInverseSolve(x,y,residual,discrepancyLin)
# except Exception as e:
# theta *= self.params.kappaTrust
# msg('Exception during linear inverse solve:\n%s\nShriking theta to %g.',str(e),theta)
# continue
# forward_problem.evalFandLinearize(x,out=Fx)
# residual[:] = y
# residual -= Fx
Td = forward_problem.T(d,out=Td)
Jp = -forward_problem.rangeIP(Td,residual)
if Jp >= 0:
theta *= self.params.kappaTrust
msg('Model problem found an uphill direction. Shrinking theta to %g.',theta)
continue
# % Sometimes in the initial stages, the linearization asks for an adjustment many orders of magnitude
# % larger than the size of the coefficient gamma. This is due to the very shallow derivatives
# % in the coefficent function (and hence large derivatives in its inverse). We've been
# % guaranteed that dh is pointing downhill, so scale it so that its size is on the order
# % of the size of the current gamma and try it out. If this doesn't do a good job, we'll end
# % up reducing theta later.
# if (params.forceGammaPositive)
self.temper_d(x,d,y,residual)
self.iterationHook(count,x,Fx,y,d,residual,Td)
# Do a linesearch in the determined direction.
Phi = lambda t: line_searchee.eval(x,d,y,t)
Jx = 0.5*forward_problem.rangeIP(residual,residual)
line_search.search(Phi,Jx,Jp,1)
if line_search.error():
msg('Linesearch failed: %s. Shrinking theta.', line_search.errMsg);
theta *= self.params.kappaTrust
continue
discrepancyPrev = discrepancy
t = line_search.value.t
x.axpy(t,d)
forward_problem.evalFandLinearize(x,out=Fx,guess=Fx)
residual.set(y)
residual -= Fx
discrepancy = self.discrepancy(x,y,residual);
self.xUpdateHook(count,x,Fx,y,residual)
# Check to see if we did a good job reducing the misfit (compared to the amount that we asked
# the linearized problem to correct).
rho = (discrepancy-discrepancyPrev)/(discrepancyLin-discrepancyPrev)
# assert(rho>0)
# Determine if the trust region requires any adjustment.
if rho > self.params.rhoLow:
# We have a good fit.
if rho > self.params.rhoHigh:
if theta < self.params.thetaMax:
theta = min(theta/self.params.kappaTrust, self.params.thetaMax);
msg('Very good decrease (rho=%g). New theta %g', rho, theta);
else:
msg('Very good decrease (rho=%g). Keeping theta %g', rho, theta);
else:
msg('Reasonable decrease (rho=%g). Keeping theta %g',rho, theta);
else:
# We have a lousy fit. Try asking for less correction.
theta = theta * params.kappaTrust;
msg('Poor decrease (rho=%g) from %g to %g;', rho, discrepancyPrev, discrepancy)
msg('wanted %g. New theta %g', discrepancyLin, theta);
# except Exception as e:
# # Store the current x and y values in case they are interesting to the caller, then
# # re-raise the exception.
# self.finalState = self.finalize(x,Fx)
# raise e
return self.finalize(x, Fx)
def search(self, F, F0, F0p, t0):
self.code = -1
self.errMsg = 'no error'
self.F = F
params = self.params
z = Bunch(F=F0, Fp=F0p, t=0, data=None)
assert F0p <= 0
# % Set up constants for checking Wolfe conditions.
self.wolfe_lo = params.sigma * z.Fp
self.wolfe_hi = params.delta * z.Fp
self.awolfe_hi = (2 * params.delta - 1) * z.Fp
self.fpert = z.F + params.epsilon
self.f0 = z.F
if params.verbose: msg('starting at z=%g (%g,%g)', z.t, z.F, z.Fp)
while True:
c = self.eval(t0)
if not numpy.isnan(c.F):
break
msg('Hit a NaN in initial evaluation at t=%g', t0)
t0 *= 0.5
if params.verbose: msg('initial guess c=%g (%g,%g)', c.t, c.F, c.Fp)
if self.wolfe(c):
if params.verbose: msg('done at init')
self.setDone(c)
return
(aj, bj) = self.bracket(z, c)
if params.verbose: msg('initial bracket %g %g', aj.t, bj.t)
if self.code >= 0:
self.doneMsg('initial bracket')
return
if params.debug: self.verifyBracket(aj, bj)
count = 0
while True:
count += 1
if count > params.nsecant:
self.setError('too many bisections in main loop')
return
(a, b) = self.secantsq(aj, bj)
if params.verbose: msg('secantsq a %g b %g', a.t, b.t)
if params.verbose: self.printBracket(a, b)
if self.code >= 0:
self.doneMsg('secant')
return
if (b.t - a.t) > params.gamma * (bj.t - aj.t):
(a, b) = self.update(a, b, (a.t + b.t) / 2)
if params.verbose: msg('update to a %g b %g', aj.t, bj.t)
if params.verbose: self.printBracket(a, b)
if self.code >= 0:
self.doneMsg('bisect')
return
aj = a
bj = b
def printBracket(self,a,b):
msg('a %g b %g f(a) %g fp(a) %g f(b) %g fp(b) %g fpert %g', a.t, b.t, a.F, a.Fp, b.F, b.Fp, self.fpert )
def search(self,f,f0,df0,t0=None):
INT = self.params.INT; # don't reevaluate within 0.1 of the limit of the current bracket
EXT = self.params.EXT; # extrapolate maximum 3 times the current step-size
MAX = self.params.MAX; # max 20 function evaluations per line search
RATIO = self.params.RATIO; # maximum allowed slope ratio
SIG = self.params.SIG;
RHO = SIG/2;
SMALL = 10.**-16 #minimize.m uses matlab's realmin
# SIG and RHO are the constants controlling the Wolfe-
#Powell conditions. SIG is the maximum allowed absolute ratio between
#previous and new slopes (derivatives in the search direction), thus setting
#SIG to low (positive) values forces higher precision in the line-searches.
#RHO is the minimum allowed fraction of the expected (from the slope at the
#initial point in the linesearch). Constants must satisfy 0 < RHO < SIG < 1.
#Tuning of SIG (depending on the nature of the function to be optimized) may
#speed up the minimization; it is probably not worth playing much with RHO.
d0 = df0;
fdata0 = None
if t0 is None:
t0 = 1/(1.-d0)
x3 = t0 # initial step is red/(|s|+1)
X0 = 0; F0 = f0; dF0 = df0; Fdata0=fdata0 # make a copy of current values
M = MAX
x2 = 0; f2 = f0; d2 = d0;
while True: # keep extrapolating as long as necessary
# x2 = 0; f2 = f0; d2 = d0;
f3 = f0; df3 = df0; fdata3 = fdata0;
success = 0
while (not success) and (M > 0):
try:
M = M - 1
(f3, df3, fdata3) = f(x3)
if isnan(f3) or isinf(f3) or any(isnan(df3)+isinf(df3)):
raise Exception('nan')
success = 1
except: # catch any error which occured in f
if self.params.verbose: msg('error on extrapolate. shrinking %g to %g', x3, (x2+x3)/2)
x3 = (x2+x3)/2 # bisect and try again
if f3 < F0:
X0 = x3; F0 = f3; dF0 = df3; Fdata0=fdata3 # keep best values
d3 = df3 # new slope
if d3 > SIG*d0 or f3 > f0+x3*RHO*d0 or M == 0: break # are we done extrapolati
x1 = x2; f1 = f2; d1 = d2 # move point 2 to point 1
x2 = x3; f2 = f3; d2 = d3 # move point 3 to point 2
A = 6*(f1-f2)+3*(d2+d1)*(x2-x1) # make cubic extrapolation
B = 3*(f2-f1)-(2*d1+d2)*(x2-x1)
Z = B+sqrt(complex(B*B-A*d1*(x2-x1)))
if Z != 0.0:
x3 = x1-d1*(x2-x1)**2/Z # num. error possible, ok!
else:
x3 = inf
if (not isreal(x3)) or isnan(x3) or isinf(x3) or (x3 < 0):
# num prob | wrong sign?
x3 = x2*EXT # extrapolate maximum amount
elif x3 > x2*EXT: # new point beyond extrapolation limit?
x3 = x2*EXT # extrapolate maximum amount
elif x3 < x2+INT*(x2-x1): # new point too close to previous point?
x3 = x2+INT*(x2-x1)
x3 = real(x3)
msg('extrapolating: x1 %g d1 %g x2 %g d2 %g x3 %g',x1,d1,x2,d3,x3)
while (abs(d3) > -SIG*d0 or f3 > f0+x3*RHO*d0) and M > 0:
if (d3 > 0) or (f3 > f0+x3*RHO*d0): # choose subinterval
x4 = x3; f4 = f3; d4 = d3 # move point 3 to point 4
else:
x2 = x3; f2 = f3; d2 = d3 # move point 3 to point 2
if self.params.verbose: msg('interpolating x2 %g x4 %g f2 %g f4 %g wolfef %g d2 %g d4 %g wolfed %g ',x2,x4,f2,f4,f0+x3*RHO*d0,d2,d4,-SIG*d0)
if f4 > f0:
x3 = x2-(0.5*d2*(x4-x2)**2)/(f4-f2-d2*(x4-x2)) # quadratic interpolation
else:
A = 6*(f2-f4)/(x4-x2)+3*(d4+d2) # cubic interpolation
B = 3*(f4-f2)-(2*d2+d4)*(x4-x2)
if A != 0:
x3=x2+(sqrt(B*B-A*d2*(x4-x2)**2)-B)/A # num. error possible, ok!
else:
x3 = inf
if isnan(x3) or isinf(x3):
x3 = (x2+x4)/2 # if we had a numerical problem then bisect
x3 = max(min(x3, x4-INT*(x4-x2)),x2+INT*(x4-x2)) # don't accept too close
(f3, df3, fdata3) = f(x3);
d3 =df3;
M = M - 1; # count epochs
if f3 < F0:
X0 = x3; F0 = f3; dF0 = df3; Fdata0 = fdata3 # keep best values
if (abs(d3) < -SIG*d0) and (f3 < f0+x3*RHO*d0): # if line search succeeded
self.code = 0
self.value = Bunch(F=f3,Fp=d3,t=x3,data=fdata3)
self.errMsg = ""
else:
self.code = 1
if M == 0:
self.errMsg = 'Too many function evaluations (>%d)' % MAX
else:
self.errMsg = 'unknown error';
self.value = Bunch(F=f0,Fp=dF0,t=X0,data=Fdata0)
def printBracket(self, a, b):
msg('a %g b %g f(a) %g fp(a) %g f(b) %g fp(b) %g fpert %g', a.t, b.t,
a.F, a.Fp, b.F, b.Fp, self.fpert)
def solve(self,x0,y,*args):
(x,y) = self.initialize(x0,y,*args)
forward_problem = self.forwardProblem()
Tx = forward_problem.T(x)
r = y.copy()
r -= Tx
cg_reset = x.size()
if (self.params.cg_reset != 0): cg_rest = self.params.cg_reset
TStarR = forward_problem.TStar(r)
normsq_TStarR = forward_problem.domainIP(TStarR,TStarR)
# d = T^* r
d = TStarR.copy()
# Eventual storage for T(d)
Td = None
count = 0
while True:
if count > self.params.ITER_MAX:
raise IterationCountFailure(self.params.ITER_MAX)
break
count += 1
if self.stopConditionMet(count,x,y,r):
msg('done at iteration %d', count)
break
Td = forward_problem.T(d,out=Td)
self.iterationHook(count, x, y, d, r, Td, TStarR)
alpha = normsq_TStarR/forward_problem.rangeIP(Td,Td)
if ((count+1 % cg_reset) == 0):
msg('resetting cg via steepest descent')
alpha = 1
# x = x + alpha*d
x.axpy(alpha,d)
# r = r - alpha*Td
r.axpy(-alpha,Td)
# beta = ||r_{k+1}||^2 / ||r_k||^2
prev_normsq_TStarR = normsq_TStarR
TStarR = forward_problem.TStar(r,out=TStarR)
normsq_TStarR = forward_problem.domainIP(TStarR,TStarR)
beta = normsq_TStarR/prev_normsq_TStarR
if ((count+1 % cg_reset) == 0): beta = 0
if (self.params.steepest_descent):
beta = 0
# d = T*r + beta*d
d *= beta
d += TStarR
Tx = forward_problem.T(x, out=Tx)
return self.finalize(x,Tx)
def search(self, f, f0, df0, t0=None):
INT = self.params.INT
# don't reevaluate within 0.1 of the limit of the current bracket
EXT = self.params.EXT
# extrapolate maximum 3 times the current step-size
MAX = self.params.MAX
# max 20 function evaluations per line search
RATIO = self.params.RATIO
# maximum allowed slope ratio
SIG = self.params.SIG
RHO = SIG / 2
SMALL = 10.**-16 #minimize.m uses matlab's realmin
# SIG and RHO are the constants controlling the Wolfe-
#Powell conditions. SIG is the maximum allowed absolute ratio between
#previous and new slopes (derivatives in the search direction), thus setting
#SIG to low (positive) values forces higher precision in the line-searches.
#RHO is the minimum allowed fraction of the expected (from the slope at the
#initial point in the linesearch). Constants must satisfy 0 < RHO < SIG < 1.
#Tuning of SIG (depending on the nature of the function to be optimized) may
#speed up the minimization; it is probably not worth playing much with RHO.
d0 = df0
fdata0 = None
if t0 is None:
t0 = 1 / (1. - d0)
x3 = t0 # initial step is red/(|s|+1)
X0 = 0
F0 = f0
dF0 = df0
Fdata0 = fdata0 # make a copy of current values
M = MAX
x2 = 0
f2 = f0
d2 = d0
while True: # keep extrapolating as long as necessary
# x2 = 0; f2 = f0; d2 = d0;
f3 = f0
df3 = df0
fdata3 = fdata0
success = 0
while (not success) and (M > 0):
try:
M = M - 1
(f3, df3, fdata3) = f(x3)
if isnan(f3) or isinf(f3) or any(isnan(df3) + isinf(df3)):
raise Exception('nan')
success = 1
except: # catch any error which occured in f
if self.params.verbose:
msg('error on extrapolate. shrinking %g to %g', x3,
(x2 + x3) / 2)
x3 = (x2 + x3) / 2 # bisect and try again
if f3 < F0:
X0 = x3
F0 = f3
dF0 = df3
Fdata0 = fdata3 # keep best values
d3 = df3 # new slope
if d3 > SIG * d0 or f3 > f0 + x3 * RHO * d0 or M == 0:
break # are we done extrapolati
x1 = x2
f1 = f2
d1 = d2 # move point 2 to point 1
x2 = x3
f2 = f3
d2 = d3 # move point 3 to point 2
A = 6 * (f1 - f2) + 3 * (d2 + d1) * (x2 - x1
) # make cubic extrapolation
B = 3 * (f2 - f1) - (2 * d1 + d2) * (x2 - x1)
Z = B + sqrt(complex(B * B - A * d1 * (x2 - x1)))
if Z != 0.0:
x3 = x1 - d1 * (x2 - x1)**2 / Z # num. error possible, ok!
else:
x3 = inf
if (not isreal(x3)) or isnan(x3) or isinf(x3) or (x3 < 0):
# num prob | wrong sign?
x3 = x2 * EXT # extrapolate maximum amount
elif x3 > x2 * EXT: # new point beyond extrapolation limit?
x3 = x2 * EXT # extrapolate maximum amount
elif x3 < x2 + INT * (
x2 - x1): # new point too close to previous point?
x3 = x2 + INT * (x2 - x1)
x3 = real(x3)
msg('extrapolating: x1 %g d1 %g x2 %g d2 %g x3 %g', x1, d1, x2, d3,
x3)
while (abs(d3) > -SIG * d0 or f3 > f0 + x3 * RHO * d0) and M > 0:
if (d3 > 0) or (f3 > f0 + x3 * RHO * d0): # choose subinterval
x4 = x3
f4 = f3
d4 = d3 # move point 3 to point 4
else:
x2 = x3
f2 = f3
d2 = d3 # move point 3 to point 2
if self.params.verbose:
msg(
'interpolating x2 %g x4 %g f2 %g f4 %g wolfef %g d2 %g d4 %g wolfed %g ',
x2, x4, f2, f4, f0 + x3 * RHO * d0, d2, d4, -SIG * d0)
if f4 > f0:
x3 = x2 - (0.5 * d2 * (x4 - x2)**2) / (
f4 - f2 - d2 * (x4 - x2)) # quadratic interpolation
else:
A = 6 * (f2 - f4) / (x4 - x2) + 3 * (d4 + d2
) # cubic interpolation
B = 3 * (f4 - f2) - (2 * d2 + d4) * (x4 - x2)
if A != 0:
x3 = x2 + (sqrt(B * B - A * d2 * (x4 - x2)**2) -
B) / A # num. error possible, ok!
else:
x3 = inf
if isnan(x3) or isinf(x3):
x3 = (x2 + x4) / 2 # if we had a numerical problem then bisect
x3 = max(min(x3, x4 - INT * (x4 - x2)),
x2 + INT * (x4 - x2)) # don't accept too close
(f3, df3, fdata3) = f(x3)
d3 = df3
M = M - 1
# count epochs
if f3 < F0:
X0 = x3
F0 = f3
dF0 = df3
Fdata0 = fdata3 # keep best values
if (abs(d3) < -SIG * d0) and (
f3 < f0 + x3 * RHO * d0): # if line search succeeded
self.code = 0
self.value = Bunch(F=f3, Fp=d3, t=x3, data=fdata3)
self.errMsg = ""
else:
self.code = 1
if M == 0:
self.errMsg = 'Too many function evaluations (>%d)' % MAX
else:
self.errMsg = 'unknown error'
self.value = Bunch(F=f0, Fp=dF0, t=X0, data=Fdata0)
def solve(self, x0, y, *args):
"""Main routine to solve the inverse problem F(x)=y. Initial guess is x=x0.
Extra arguments are passed to :func:`initialize`."""
(x, y, targetDiscrepancy) = self.initialize(x0, y, *args)
self.discrepancy_history = []
forward_problem = self.forwardProblem()
params = self.params
cg_reset = x.size()
if (self.params.cg_reset != 0): cg_reset = self.params.cg_reset
# Initial functional evalutation
Fx = forward_problem.evalFandLinearize(x)
# Prepare some storage
Td = None
residual = y.copy()
residual.axpy(-1, Fx)
discrepancy = self.discrepancy(x, y, residual)
# The class that performs our linesearches.
line_search = linesearchHZ.LinesearchHZ(params=self.params.linesearch)
line_searchee = ForwardProblemLineSearchAdaptor(forward_problem)
# Main loop
count = 0
theta = params.thetaMax
# try:
for kkkkk in range(1):
while True:
self.discrepancy_history.append(discrepancy)
if count > self.params.ITER_MAX:
raise IterationCountFailure(self.params.ITER_MAX)
count += 1
if theta < self.params.thetaMin:
raise NumericalFailure(
'Reached smallest trust region size.')
# Error to correct:
discrepancyLin = (
1 - theta) * discrepancy + theta * targetDiscrepancy
msg(
'(%d) discrepancy: current %g linear goal:%g goal: %g\n---',
count, discrepancy, discrepancyLin, targetDiscrepancy)
if discrepancy <= self.params.mu * targetDiscrepancy:
msg('done at iteration %d', count)
break
# try:
d = self.linearInverseSolve(x, y, residual, discrepancyLin)
# except Exception as e:
# theta *= self.params.kappaTrust
# msg('Exception during linear inverse solve:\n%s\nShriking theta to %g.',str(e),theta)
# continue
# forward_problem.evalFandLinearize(x,out=Fx)
# residual[:] = y
# residual -= Fx
Td = forward_problem.T(d, out=Td)
Jp = -forward_problem.rangeIP(Td, residual)
if Jp >= 0:
theta *= self.params.kappaTrust
msg(
'Model problem found an uphill direction. Shrinking theta to %g.',
theta)
continue
# % Sometimes in the initial stages, the linearization asks for an adjustment many orders of magnitude
# % larger than the size of the coefficient gamma. This is due to the very shallow derivatives
# % in the coefficent function (and hence large derivatives in its inverse). We've been
# % guaranteed that dh is pointing downhill, so scale it so that its size is on the order
# % of the size of the current gamma and try it out. If this doesn't do a good job, we'll end
# % up reducing theta later.
# if (params.forceGammaPositive)
self.temper_d(x, d, y, residual)
self.iterationHook(count, x, Fx, y, d, residual, Td)
# Do a linesearch in the determined direction.
Phi = lambda t: line_searchee.eval(x, d, y, t)
Jx = 0.5 * forward_problem.rangeIP(residual, residual)
line_search.search(Phi, Jx, Jp, 1)
if line_search.error():
msg('Linesearch failed: %s. Shrinking theta.',
line_search.errMsg)
theta *= self.params.kappaTrust
continue
discrepancyPrev = discrepancy
t = line_search.value.t
x.axpy(t, d)
forward_problem.evalFandLinearize(x, out=Fx, guess=Fx)
residual.set(y)
residual -= Fx
discrepancy = self.discrepancy(x, y, residual)
self.xUpdateHook(count, x, Fx, y, residual)
# Check to see if we did a good job reducing the misfit (compared to the amount that we asked
# the linearized problem to correct).
rho = (discrepancy - discrepancyPrev) / (discrepancyLin -
discrepancyPrev)
# assert(rho>0)
# Determine if the trust region requires any adjustment.
if rho > self.params.rhoLow:
# We have a good fit.
if rho > self.params.rhoHigh:
if theta < self.params.thetaMax:
theta = min(theta / self.params.kappaTrust,
self.params.thetaMax)
msg('Very good decrease (rho=%g). New theta %g',
rho, theta)
else:
msg(
'Very good decrease (rho=%g). Keeping theta %g',
rho, theta)
else:
msg('Reasonable decrease (rho=%g). Keeping theta %g',
rho, theta)
else:
# We have a lousy fit. Try asking for less correction.
theta = theta * params.kappaTrust
msg('Poor decrease (rho=%g) from %g to %g;', rho,
discrepancyPrev, discrepancy)
msg('wanted %g. New theta %g', discrepancyLin, theta)
# except Exception as e:
# # Store the current x and y values in case they are interesting to the caller, then
# # re-raise the exception.
# self.finalState = self.finalize(x,Fx)
# raise e
return self.finalize(x, Fx)
def doneMsg(self,where):
if self.code > 0:
msg('done at %s with error status: %s', where, self.errMsg);
else:
if self.params.verbose: msg('done at %s with val=%g (%g, %g)', where, self.value.t, self.value.F, self.value.Fp );
