SciPy Optimization Methods¶
Several methods from the optimization module of the popular SciPy [1] library are available. For details of the algorithms see the `SciPy manual`_. .. _SciPy manual: https://docs.scipy.org/doc/scipy/reference/tutorial/optimize.html Method names are based on SciPy API’s naming scheme.
Gradient-free methods¶
These algorithms do not make use of the objective function gradient. The objective function or setup.function, in the case of Python evaluation, should just return a single float that will be interpreted as objective function value at the observation point.
Nelder-Mead: The well known Nelder-Mead method, sometimes just referred to as “simplex method”.
COBYLA: Constrained Optimization BY Linear Approximations. This is another trust-region method. COBYLA generally supports inequality and equality constraints, however, rsopt does not have an interface to pass constrains at this time.
Gradient-based methods¶
These require passing gradient information for the objective function at the observation point. For these methods the objective function or setup.function, in the case of Python evaluation, should return a tuple of (f, fgrad) where f is the value of the objective function at the observation point x as a float and fgrad is the gradient of f at x, fgrad should be an array of floats with the same dimension as x.
BFGS: The Broyden-Fletcher-Goldfarb-Shanno algorithm. Can also be used like a gradient free method. If no gradient information is passed then BFGS will use a first-difference estimate.