<table cellspacing="0" cellpadding="0" border="0" ><tr><td valign="top" style="font: inherit;">> Also I am not member of the IPOPT-Team:<br>><br>> You do not need the Hessian if you use quasi-newtown approximation of <br>> second derivatives (LBFGS). Set the option<br>> hessian_approximation=limited-memory<br>><br>> However you need the Jacobian.<br>> If you have a set of constraints and are not sure which constraints are <br>> used, you could define the Jacobian structure as being all possible <br>> entries of any element.<br>><br>> If you do not have the Jacobian functions you either have to do finite <br>> difference approximation (which might lead to slow evaluation and <br>> convergence problems) or automatic differentiation tool (I have heard<br> <br>> about ADOL-C but never used it).<br>><br>> Best Regards,<br>> Uwe<br>> <br>Hi!<br>Thank you for your reply !<br><br>I have
set this option and run it properly. <br>I do not have a clear concept on optimization. <br>Do there have any big difference between the default method than the quasi-newton approx. of second derivatives?<br>Will it decrease the accuracy of result? or it will only increase the number of iteration?<br><br>Thank you so much for your help!<br><br>Best Regards,<br>Lewis<br><br></td></tr></table><br>