Nonlinear optimization with upper bound on the norm

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I have an optimization problem

min(f(x))
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?

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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.

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