Implement two targets: higher and lower. Choose which one to go to depending on first guess.
Different calculation of next_obj_target

TODO: change 1.5 to magic factor

Implemented adaptive max and min steps in profiling. If the optimization during profiling fails (results in inf value), the algorithm will first try to iteratively decrease `max_step_size` to be closer to the last point that had a successful optimization. If that doesn't work (if we reduce max_step_size below min_step_size), then max_step_size is set back to the default and we try to increase min_step_size to "jump over" the problematic area.

Resampling random points and start from those is only the last resort and will be done if these two do not work. The idea is that we want to stay as close as we can to the last profiling point.

TODO: Put the adaptive reduction/increase of max_step_size/min_step_size into options

- BUGFIX: absolute value in objective targets at last_delta_fval
- BUGFIX: extrapolation explosions if we start at boundary
- Feature: Trust region on extrapolation
- Added y ticks back into the plot, sometimes the range is completely different.
- Added points to the plotting of profiles (in case of one result and one profile list id)
- Added color change to plotting of profiles (in case of one result and one profile list id)
- LOGGING: added logging.INFO with informations of steps made and successful optimizations.

Test were testing whether the profiling method was making a lot of steps. Now we're making a lot more steps due to higher robustness.

…into change_profiling

Co-authored-by: Maren Philipps <[email protected]>