cobyqa.OptimizeResult

class cobyqa.OptimizeResult

Result of the optimization algorithm.

Attributes:

xnumpy.ndarray, shape (n,)

Solution point.

successbool

Flag indicating whether the optimizer terminated successfully.

statusint

Termination status.

messagestr

Description of the termination status.

funfloat

Value of the objective function.

jacnumpy.ndarray, shape (n,)

Approximation of the gradient of the objective function based on undetermined interpolation. If the value of a component (or more) of the gradient is unknown, it is replaced with numpy.nan.

nfevint

Number of function evaluations.

nitint

Number of iterations performed by the optimizer.

maxcvfloat

Maximum constraint violation. It is set only if the problem is not declared unconstrained by the optimizer.

Methods

clear()
copy()
fromkeys(iterable[, value])	Create a new dictionary with keys from iterable and values set to value.
get(key[, default])	Return the value for key if key is in the dictionary, else default.
items()
keys()
pop(key[, default])	If the key is not found, return the default if given; otherwise, raise a KeyError.
popitem(/)	Remove and return a (key, value) pair as a 2-tuple.
setdefault(key[, default])	Insert key with a value of default if key is not in the dictionary.
update([E, ]**F)	If E is present and has a .keys() method, then does: for k in E: D[k] = E[k] If E is present and lacks a .keys() method, then does: for k, v in E: D[k] = v In either case, this is followed by: for k in F: D[k] = F[k]
values()