Given a function f(x) that takes an input vector x and returns a vector of the same length, how can you find the roots of the function setting constraints on x? (E.g. a range for each component of x.)
To my surprise I could not find a lot of useful information about this. In the scipy list for Optimization and Root finding algorithms there seem to be some options for scalar functions such as brentq. I can not find any algorithms that supports such an option for the multivariate case though.
Of course one could do a work-around like squaring each component of the returned vector and then use one of the minimizers such as differential_evolution (this is the only one I think actually). I can not imagine that this is a good strategy though, since it kills the quadratic convergence of Newton's algorithm. Also I find it really surprising that there does not seem to be an option for this, since it must be a really common problem. Have I missed something?