[Minotaur] FilterSQP engine strange behaviour

[Roberto Chao]

Hello, my name is Roberto. I'm an experienced C/C++/Java developer and
I'm currently very excited with the SNQP method and Minotaur seems to
me the perfect interface with filtersqp solver.

I've downloaded an successfully compiled minotaur-0.2.0 on a
Linux-x86_64 machine. The executable has successfully passed all unit
tests. I'd like to use minotaur to solve small-medium scale (tens of
constraints) non-linear programming problems through the SNQP method.

The first basic problem I've tried to solve has been taken from the
book: Practical Methods of Optimization (2nd edition) (Pag 296):

max x1+x2
st. x1^2+x2^2<=1

the FilterSQPEngine exhibits a strange behaviour:

filterSQP: version 20010817
(x0, x1) = (2, 0)
(x0, x1) = (1.25, 10)
(x0, x1) = (1.25, 5)
(x0, x1) = (2.06029, 2.24118)
(x0, x1) = (1.23322, 1.157)
(x0, x1) = (0.786654, 0.829407)
(x0, x1) = (0.720027, 0.707684)
(x0, x1) = (0.706927, 0.707408)
(x0, x1) = (0.707107, 0.707107)
FilterSQPEngine: total calls            = 1
FilterSQPEngine: strong branching calls = 0
FilterSQPEngine: total time in solving  = 0.003292
FilterSQPEngine: time in str branching  = 0
FilterSQPEngine: total iterations       = 8
FilterSQPEngine: strong br iterations   = 0
solution status code = 1
solution status = ProvenLocalOptimal

However using IPopt engine gives me this result:

(x0, x1) = (2, 0)
(x0, x1) = (1.24837, 1.7)
(x0, x1) = (1.06331, 1.06783)
(x0, x1) = (0.831419, 0.831195)
(x0, x1) = (0.731859, 0.731869)
(x0, x1) = (0.708612, 0.708612)
(x0, x1) = (0.707108, 0.707108)
(x0, x1) = (0.707107, 0.707107)
Ipopt: total calls            = 1
Ipopt: strong branching calls = 0
Ipopt: total time in solving  = 0.047418
Ipopt: total time in presolve = 2e-06
Ipopt: time in str branching  = 0
Ipopt: total iterations       = 7
Ipopt: strong br iterations   = 0
solution status code = 1
solution status = ProvenLocalOptimal

so I can assume I've correctly defined jacobian, hessian of lagrange,
objective an constraint evaluation callback methods.

Can you help me please?
Thank you in advance.
Roberto Chao