[Ipopt] Matlab crashes when infeasible solution is found

[Ray Zimmerman]

It might be related to the libraries it attempts to use to print to the console. Can you get it to print progress successfully for a feasible problem?

-- 
Ray Zimmerman
Senior Research Associate
419A Warren Hall, Cornell University, Ithaca, NY 14853
phone: (607) 255-9645




On Jan 10, 2012, at 10:54 AM, Siddharth Goyal wrote:

> Hi,
> 
> I have a precompiled ipopt.mexmaci64 file which was complied on a different system. I am able to run all the predefine examples (eg. examplehs051,...) with the mex file on my system. However, when there is no feasible solution Matlab crashes. Is it because mex file was complied on another system? If not, what can be the possible reason?
> 
> BTW, I was trying to solve a simple problem, 
> min x1^2+x2^2
> such that x1+3X2 =0,
> 1<x1,x2<10
> 
> I have used the following code.
> 
> function [x, info] = examplehs051
> 
>   x0         = [ 2.5 0.5 ];  % The starting point.
>    options.lb = [1 1 ];  % Lower bound on the variables.
>   options.ub = [+10 +10];  % Upper bound on the variables.
> 
>   options.cl = [ 0 ];             % Lower bounds on constraints.
>   options.cu = [ 0 ];             % Upper bounds on constraints.
> 
>   % Set the IPOPT options.
>   options.ipopt.jac_c_constant        = 'yes';
>   options.ipopt.hessian_approximation = 'limited-memory';
>   options.ipopt.mu_strategy           = 'adaptive';
>   options.ipopt.tol                   = 1e-7;
> 
>   % The callback functions.
>   funcs.objective         = @objective;
>   funcs.constraints       = @constraints;
>   funcs.gradient          = @gradient;
>   funcs.jacobian          = @jacobian;
>   funcs.jacobianstructure = @jacobian;
>   
>   % Run IPOPT.
>   [x info] = ipopt(x0,funcs,options);
>   
> % ----------------------------------------------------------------------
> function f = objective (x)
>   f = x(1)^2 + x(2)^2;
> 
> % ----------------------------------------------------------------------
> function g = gradient (x)
>   g = 2*[ x(1) ;
> 	  x(2) ]';
> % ----------------------------------------------------------------------
> function c = constraints (x)
>   c = [ x(1) + 3*x(2)   ];
> 
> % ----------------------------------------------------------------------
> function J = jacobian (x)  
>   J = sparse([ 1  +3 ]);
> 
> 
> Any suggestion is highly appreciated
> Thanks
> 
> Siddharth
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