Dear PETSc experts,
I’m trying to set up a simple constrained optimization problem with
petsc4py.TAO and I’m hitting a
[0]PETSC ERROR: Caught signal number 11 SEGV: Segmentation Violation, probably
memory access out of range
Below is a minimal failing example (which also highlights how little I know
about PETSc :) ). I hit this error because I was trying to understand how to
assign values to the Jacobian of the equality constraint in the function
myEqualityJacobian. Any advice on how to avoid the segmentation violation (and
more generally, any advice about how to code this up better) are warmly
welcomed.
import petsc4py.PETSc as PETSc
# initial guess
x = PETSc.Vec().createSeq(2)
x.set(0)
x.assemble()
print("initial guess: ", x.array_r)
# create TAO solver and set solution
solver = PETSc.TAO().create()
solver.setType("almm")
solver.setFromOptions()
# add objective and gradient routines
solver.setSolution(x)
def myObjGrad(tao, x, g):
J = x[0]**2 + x[1]**2
dJ = 2*x
dJ.copy(g)
return J
solver.setObjectiveGradient(myObjGrad, None)
# add equality constraint and its derivative
def myEqualityConstraint(tao, x, ce):
ce.set((x[0]-2)**2 + x[1]**2)
ce = PETSc.Vec().createSeq(1)
ce.assemble()
solver.setEqualityConstraints(myEqualityConstraint, ce)
def myEqualityJacobian(tao, x, JE, JEpre):
v = PETSc.Vec().createSeq(2)
v.setValue(0, 4)
v.assemble()
vals = 2*x - v
# How should I enter vals into JE?
col = JE.getColumnVector(0)
vals.copy(col)
#col = JE.getDenseColumnVec(0)
#JE.restoreDenseColumnVec(0)
solver.setJacobianEquality(myEqualityJacobian)
# solve the problem
solver.solve()
print("found optimum at: ", solver.getSolution().array_r)
solver.destroy()
Many thanks in advance for your help.
Best,
Alberto
