8000 Much cleaner implementation. · PythonOptimizers/NLP.py@4fa3468 · GitHub
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Andrew Lambedpo
Andrew Lambe
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dpo
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Much cleaner implementation.
We now have the user pass a QuasiNewtonModel object to AugLag, which is then converted to a SlackModel internally. The SlackModel class already implements the correct Hessian-vector product for the matrix structure. This avoids the definition of a whole bunch of specialized classes. Only TRON is subclassed to compute the correct set of update vectors. The full quasi-Newton approximation of the AugmentedLagrangian model is still available upon request.
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‎nlp/model/augmented_lagrangian.py

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@@ -176,27 +176,3 @@ class QuasiNewtonAugmentedLagrangian(QuasiNewtonModel, AugmentedLagrangian):
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"""
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pass # All the work is done by the parent classes.
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class StructuredQuasiNewtonAugmentedLagrangian(QuasiNewtonModel, AugmentedLagrangian):
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"""Bound-constrained augmented Lagrangian with a structured Hessian.
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Overwrite only the hprod method to provide a structured estimate of
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the Hessian in quadratic approximations.
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"""
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def __init__(self, *args, **kwargs):
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super(StructuredQuasiNewtonAugmentedLagrangian, self).__init__(*args, **kwargs)
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def hprod(self, x, z, v, **kwargs):
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"""Structured Hessian-vector product."""
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model = self.model
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# cons = self.model.cons(x)
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# w = model.hprod(x, self.pi - self.penalty * cons, v)
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w = self.H * v
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J = model.jop(x)
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Hv = w + self.penalty * J.T * J * v
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if self.prox > 0:
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Hv += self.prox * v
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return Hv

‎nlp/model/snlp.py

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@@ -282,4 +282,4 @@ def ghivprod(self, x, g, v, **kwargs):
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gHiv = np.zeros(self.m)
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gHiv[:om] = model.ghivprod(x[:on], g[:on], v[:on], **kwargs)
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return gHiv
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return gHiv

‎nlp/optimize/auglag.py

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@@ -11,7 +11,8 @@
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import logging
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from nlp.model.augmented_lagrangian import AugmentedLagrangian
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from nlp.model.augmented_lagrangian import QuasiNewtonAugmentedLagrangian
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from nlp.model.augmented_lagrangian import StructuredQuasiNewtonAugmentedLagrangian
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# from nlp.model.augmented_lagrangian import StructuredQuasiNewtonAugmentedLagrangian
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from nlp.model.qnmodel import QuasiNewtonModel
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from nlp.optimize.pcg import TruncatedCG
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from nlp.tools.exceptions import UserExitRequest
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from nlp.tools.utils import project, where
@@ -78,12 +79,9 @@ def __init__(self, model, bc_solver, **kwargs):
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:stal: Problem converged to an infeasible point
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:time: Time limit exceeded
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"""
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self.qn = kwargs.get("qn","none")
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if self.qn == "full":
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full_qn = kwargs.get("full_qn",False)
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if full_qn:
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self.model = QuasiNewtonAugmentedLagrangian(model, **kwargs)
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elif self.qn == "struct":
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self.model = StructuredQuasiNewtonAugmentedLagrangian(model, **kwargs)
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else:
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self.model = AugmentedLagrangian(model, **kwargs)
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‎nlp/optimize/tron.py

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@@ -604,11 +604,12 @@ def __init__(self, *args, **kwargs):
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def post_iteration(self, **kwargs):
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if self.step_accepted:
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# Form correction to the gradient difference to account for structure
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on = self.model.model.original_n
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cons_new = self.model.model.cons(self.x)
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cons_old = self.model.model.cons(self.x_old)
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J_old = self.model.model.jop(self.x_old)
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penalty = self.model.penalty
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dgrad_mod = self.dgrad + penalty * J_old.T * (cons_old - cons_new)
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self.model.H.store(self.dvars, self.dgrad, dgrad_mod)
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self.model.model.model.H.store(self.dvars[:on], self.dgrad[:on], dgrad_mod[:on])

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