[Ipopt] Achieving small step size in the optimizatiton

[lorejam at liralab.it]

Dear all,
I am using IpIpot (since some years) to compute the inverse kinematics of
humanoid robots. Due to the system redundancy, the inversion is formulated
as an optimization problem.

I am now treating a specif problem in which I need to directly control the
step size of the gradient descent during the optimization (i.e.
alpha_primal).
In particular, considering the update of the solution at the i-th
iteration , x_i+1 = x_i + d_x , I need d_x to be limited (and , typically,
quite small).
Even at the cost of reducing the convergence speed, that's what I need.

Do any of you has an idea of how to implement this behavior?

I am using a precompiled version of IpOpt (v. 3.7) for Windows, so I would
like to achieve this WITHOUT changing the IpOpt source code, but only
manipulating the parameters or the interface to my TNLP.

Indeed, I tried to compile IpOpt for Windows but I got several issues, and
therefore I'd like to keep using the precompiled version, if possible.

Any suggestion would be highly appreciated!
Thank you!

Best regards,
Lorenzo