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Update IRLS directive to allow multiple GN iterations.
Remove modifications to the ProjGN solver. Update IRLS example.
This commit is contained in:
D Fournier
+82
-74
@@ -144,34 +144,6 @@ class BetaSchedule(InversionDirective):
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if self.debug: print 'BetaSchedule is cooling Beta. Iteration: %d' % self.opt.iter
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self.invProb.beta /= self.coolingFactor
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#class BetaSchedule_PGN_CG(InversionDirective):
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# """BetaSchedule"""
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#
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# coolingFactor = 5.
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# coolingRate = 1
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# GN_step_last = None
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# GN_step_c = None
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#
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# def endIter(self):
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#
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# """ Compute the change in GN step, and proceed with cooling if below tol"""
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# if self.opt.iter == 1:
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# self.GN_step_last = np.linalg.norm(self.opt.xc - self.opt.x_last)
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# d_GN_step = 1.
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#
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# else:
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# self.GN_step_c = np.linalg.norm(self.opt.xc - self.opt.x_last)
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# d_GN_step = self.GN_step_c / self.GN_step_last
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#
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# # Re-initiate last GN step
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# self.GN_step_last = self.GN_step_c
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#
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# print "GN_step_last: ", self.GN_step_last
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# print "d_GN_step: ", d_GN_step
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#
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# if self.opt.iter > 0 and self.opt.iter % self.coolingRate == 0:
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# if self.debug: print 'BetaSchedule is cooling Beta. Iteration: %d' % self.opt.iter
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# self.invProb.beta /= self.coolingFactor
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class TargetMisfit(InversionDirective):
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@@ -286,10 +258,16 @@ class Update_IRLS(InversionDirective):
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gamma = None
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phi_m_last = None
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phi_d_last = None
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f_old = None
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f_min_change = 1e-1
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coolingRate = 3
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maxIRLSiter = 10
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def initialize(self):
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self.IRLSiter = 0
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# Scale the regularization for changes in norm
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if getattr(self, 'phi_m_last', None) is not None:
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@@ -310,48 +288,75 @@ class Update_IRLS(InversionDirective):
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self.reg._Wx = None
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self.reg._Wy = None
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self.reg._Wz = None
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if getattr(self, 'phi_d_last', None) is None:
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self.phi_d_last = self.invProb.phi_d
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if getattr(self, 'f_last', None) is None:
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self.f_old = self.invProb.evalFunction(self.reg.curModel, return_g=False, return_H=False)
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print self.f_old
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def endIter(self):
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# Cool the threshold parameter if required
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if getattr(self, 'factor', None) is not None:
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eps = self.reg.eps / self.factor
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# Only update after GN iterations
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if self.opt.iter % self.coolingRate == 0:
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self.IRLSiter += 1
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if getattr(self, 'eps_min', None) is not None:
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self.reg.eps = np.max([self.eps_min,eps])
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else:
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self.reg.eps = eps
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# Get phi_m at the end of current iteration
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self.phi_m_last = self.invProb.phi_m_last
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# Reset the regularization matrices so that it is
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# recalculated for current model
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self.reg._Wsmall = None
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self.reg._Wx = None
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self.reg._Wy = None
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self.reg._Wz = None
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# Update the model used for the IRLS weights
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self.reg.curModel = self.invProb.curModel
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# Temporarely set gamma to 1. to get raw phi_m
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self.reg.gamma = 1.
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# Compute new model objective function value
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phim_new = self.reg.eval(self.invProb.curModel)
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# Update gamma to scale the regularization between IRLS iterations
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self.reg.gamma = self.phi_m_last / phim_new
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# Reset the regularization matrices again for new gamma
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self.reg._Wsmall = None
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self.reg._Wx = None
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self.reg._Wy = None
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self.reg._Wz = None
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self.f_change = np.abs(self.f_old - self.opt.f_last) / self.f_old
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print "Function decrease" + str(self.f_change)
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# Check for maximum number of IRLS cycles
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if self.IRLSiter == self.maxIRLSiter:
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self.opt.stopNextIteration = True
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return
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# Check if the function has changed enough
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if self.f_change < self.f_min_change:
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self.opt.stopNextIteration = True
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return
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else:
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self.f_old = self.opt.f_last
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# Cool the threshold parameter if required
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if getattr(self, 'factor', None) is not None:
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eps = self.reg.eps / self.factor
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if getattr(self, 'eps_min', None) is not None:
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self.reg.eps = np.max([self.eps_min,eps])
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else:
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self.reg.eps = eps
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# Get phi_m at the end of current iteration
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self.phi_m_last = self.invProb.phi_m_last
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# Reset the regularization matrices so that it is
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# recalculated for current model
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self.reg._Wsmall = None
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self.reg._Wx = None
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self.reg._Wy = None
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self.reg._Wz = None
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# Update the model used for the IRLS weights
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self.reg.curModel = self.invProb.curModel
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# Temporarely set gamma to 1. to get raw phi_m
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self.reg.gamma = 1.
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# Compute new model objective function value
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phim_new = self.reg.eval(self.invProb.curModel)
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# Update gamma to scale the regularization between IRLS iterations
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self.reg.gamma = self.phi_m_last / phim_new
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# Reset the regularization matrices again for new gamma
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self.reg._Wsmall = None
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self.reg._Wx = None
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self.reg._Wy = None
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self.reg._Wz = None
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# Compute the change in
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class Update_lin_PreCond(InversionDirective):
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"""
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Create a Jacobi preconditioner for the linear problem
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@@ -411,11 +416,14 @@ class Scale_Beta(InversionDirective):
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update is done only if the misfit is outside some threshold bounds.
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"""
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tol = 0.05
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coolingRate=5
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def endIter(self):
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# Check if misfit is within the tolerance, otherwise adjust beta
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val = self.invProb.phi_d / (self.survey.nD*0.5)
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if np.abs(1.-val) > self.tol:
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self.invProb.beta = self.invProb.beta * self.survey.nD*0.5 / self.invProb.phi_d
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# Only update after GN iterations
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if self.opt.iter % self.coolingRate == 0:
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# Check if misfit is within the tolerance, otherwise adjust beta
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val = self.invProb.phi_d / (self.survey.nD*0.5)
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if np.abs(1.-val) > self.tol:
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self.invProb.beta = self.invProb.beta * self.survey.nD*0.5 / self.invProb.phi_d
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