[Scipy-tickets] [SciPy] #1231: use of uninitialized variable in SLSQP when n equality constraints

SciPy Trac scipy-tickets@scipy....
Mon Jul 12 17:51:07 CDT 2010


#1231: use of uninitialized variable in SLSQP when n equality constraints
----------------------------+-----------------------------------------------
 Reporter:  stevenj         |       Owner:  somebody
     Type:  defect          |      Status:  new     
 Priority:  normal          |   Milestone:  0.9.0   
Component:  scipy.optimize  |     Version:  0.7.0   
 Keywords:                  |  
----------------------------+-----------------------------------------------
 I was adapting the SLSQP code for use in another library (NLopt, ab-
 initio.mit.edu/nlopt), and I noticed a bug that you might want to fix.  In
 the case where the  number of equality constraints equals the dimension of
 the problem, valgrind reported the use of an uninitialized variable in a
 conditional statement.   I tracked the source of the uninitialized value
 down to line 813 of slsqp_optmz.f in SUBROUTINE lsei:

       CALL dcopy_ (mg-mc,w(mc1),0,w(mc1),1)

 I believe that this should be:

       CALL dcopy_ (mg,w(mc1),0,w(mc1),1)

 This initializes part of the work array w to 0.  If the number of
 inequality constraints differs from the number of dimensions (mc.NE.n in
 lsei), then this statement doesn't matter because w(mc1...mc1+mg-1) get
 overwritten anyway by the Lagrange multipliers when lsi is called a little
 later.  But if mc.EQ.n, then the GOTO statment on line 815 jumps to the
 end, where w(mc1...mc1+mg-1) is used in a dot product.

 I've verified that, with the above fix, it correctly solves a test problem
 with various numbers of equality and/or inequality constraints, and
 valgrind no longer complains.

 PS. I should also mention that, because of rounding errors, SLSQP will
 occasionally evaluate the objective/constraints slightly outside of the
 bounding box.  I'm not sure if you care about this; my NLopt library
 guarantees that the bound constraints are strictly honored (unlike
 nonlinear constraints), so I had to tweak SLSQP in a couple of places to
 check that rounding errors don't push the solution out of bounds.

-- 
Ticket URL: <http://projects.scipy.org/scipy/ticket/1231>
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