[Coin-ipopt] hessian information for optimal control problems

[saeed serpooshan]

hi
i'm very happy that see you reply my questions.. this is very good! thanks
i have a question about using ipopt for solution of an optimal control problem. i want to use my own code to discretize optimal control problem and convert it to NLP such that ipopt can solve it. for this, i use trapezoidal rule to convert differential equations of dynamic system into discrete form as a series of equality constraints: c(i,k) = x(i,k+1) - x(i,k) - h* (f(i,k)+f(i,k+1))/2  where i=[1..nx] number of states and k is index for time grid [1..nt-1]
i find derivativ of these equations analytically because it is simple. i use ipopt with options IFull=1 and IQUASI=6 such that hessian is not required by me. this approch works fine for some simple problems. (for example for an min-energy problem in bryson-ho, x(0)=x(1)=0, v(0)=1=-v(1) , minimize f=1/2*u^2 with a path constraint x<1/9 IPOPT Converges in only 11 iterations with exact solution)
but now i want to solve shuttle ReEntry problem as in NEOS Server samples solved by ipopt.
for that complicate problem my approch not converged at all. i am not sure why? is it because of lack of hessian information? how can i find that for an optimal control problem? can you help me?
 
thanks
s.serpooshan
 
 

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