[petsc-users] [11 SEGV: Segmentation Violation] petsc4py.TAO

[Barry Smith]

  Alberto,

  You need to construct a matrix and pass it in as the second argument to solver.setJacobianEquality(myEqualityJacobian, JE) 

  Barry
 
  



> On May 27, 2024, at 11:58 AM, Paganini, Alberto D.M. (Dr.) <a.paganini at leicester.ac.uk> wrote:
> 
> This Message Is From an External Sender
> This message came from outside your organization.
> 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

-------------- next part --------------
An HTML attachment was scrubbed...
URL: <http://lists.mcs.anl.gov/pipermail/petsc-users/attachments/20240527/e53c695f/attachment-0001.html>