Nonlinear optimization with upper bound on the norm

I have an optimization problem

s.t. ||x|| < 1

Where f is convex function (logarithm of sigmoid functions). The norm is L2-norm.

I wonder what methods available for this type of optimization? I have tried SLSQP and COBYLA but they seem to be very heavy solving more general problem, with a function on the inequalities and equalities, while in my case I just care for the norm.

Are there some methods that could be more light-weight (in particular, if I can integrate them with Gradient Descent) and at the same time preserve the constraints?

This is not the correct forum for this question, which is devoted to specific software packages. Please consult a more general-purpose forum like Math StackExchange.