jax.scipy.optimize.OptimizeResults#

class jax.scipy.optimize.OptimizeResults(x: jax.Array, success: Union[bool, jax.Array], status: Union[int, jax.Array], fun: jax.Array, jac: jax.Array, hess_inv: Optional[jax.Array], nfev: Union[int, jax.Array], njev: Union[int, jax.Array], nit: Union[int, jax.Array])[source]#

Object holding optimization results.

Parameters
  • x – final solution.

  • successTrue if optimization succeeded.

  • status – integer solver specific return code. 0 means converged (nominal), 1=max BFGS iters reached, 3=zoom failed, 4=saddle point reached, 5=max line search iters reached, -1=undefined

  • fun – final function value.

  • jac – final jacobian array.

  • hess_inv – final inverse Hessian estimate.

  • nfev – integer number of function calls used.

  • njev – integer number of gradient evaluations.

  • nit – integer number of iterations of the optimization algorithm.

__init__()#

Methods

__init__()

count(value, /)

Return number of occurrences of value.

index(value[, start, stop])

Return first index of value.

Attributes

fun

Alias for field number 3

hess_inv

Alias for field number 5

jac

Alias for field number 4

nfev

Alias for field number 6

nit

Alias for field number 8

njev

Alias for field number 7

status

Alias for field number 2

success

Alias for field number 1

x

Alias for field number 0