optim_doc: lm_feasible

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### 1.2 Default backend lm_feasible of scalar optimization

A Levenberg/Marquardt-like algorithm, attempting to honour constraints throughout the course of optimization. This means that the initial parameters must not violate constraints (to find an initial feasible set of parameters, e.g. core Octaves `sqp` can be used ( see octave_sqp), by specifying an objective function which is constant or which returns a norm of the distances to the initial values). The Hessian is either supplied by the user or is approximated by the BFGS algorithm. Core Octaves `sqp` performed better in some tests with unconstrained problems.

Returned value cvg will be `2` or `3` for success and `0` or `-4` for failure ( see nonlin_min for meaning). Returned structure outp will have the fields `niter`, `nobjf`, and `user_interaction`.

Backend-specific defaults are: `MaxIter`: 20, `fract_prec`: `zeros (size (parameters))`, `max_fract_change`: `Inf` for all parameters. The setting `TolX` is not honoured.

Interpretation of `Display`: if set to `"iter"`, currently only information on applying `max_fract_change` is printed.