[Minotaur] FilterSQP engine strange behaviour

[Ashutosh Mahajan]

Dear Roberto

Both FilterSQP and Ipopt seem to be converging to the same point
(x0, x1) = (0.707107, 0.707107) in roughly the same number of
iterations. Can you be a bit more specific about what you feel is unexpected?
Cheers.

--
Regards
Ashutosh Mahajan
http://www.ieor.iitb.ac.in/amahajan

On Sun, Nov 27, 2016 at 08:38:10PM +0100, Roberto Chao wrote:
> 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
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