ukiZddlmZddlmZmZddlmZmZddlm Z ddl m Z ddl mZddlmZGdd eZ d d d d  dd Zy )) annotations)CallableMapping)Any NamedTuple)numpy) minimize_bfgs)_minimize_lbfgs)ArraycleZdZUdZded<ded<ded<ded<ded <d ed <ded <ded <ded<y)OptimizeResultsa[Object holding optimization results. Parameters: x: final solution. success: ``True`` 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. r xz bool | Arraysuccessz int | Arraystatusfunjacz Array | Nonehess_invnfevnjevnitN)__name__ __module__ __qualname____doc____annotations__[/home/cdr/jupyterlab/.venv/lib/python3.12/site-packages/jax/_src/scipy/optimize/minimize.pyr r s< (   * * rr N)toloptionsc <|i}ttsd}t|jfd}|j dk(rt ||fi|}|j tj|jz} t|j| |j|j|j|j|j |j"|j$ S|j dk(rt'||fi|}|j tj|jz} t|j| |j|j|jd|j |j"|j$ St)d|d) aFMinimization of scalar function of one or more variables. This API for this function matches SciPy with some minor deviations: - Gradients of ``fun`` are calculated automatically using JAX's autodiff support when required. - The ``method`` argument is required. You must specify a solver. - Various optional arguments in the SciPy interface have not yet been implemented. - Optimization results may differ from SciPy due to differences in the line search implementation. ``minimize`` supports :func:`~jax.jit` compilation. It does not yet support differentiation or arguments in the form of multi-dimensional arrays, but support for both is planned. Args: fun: the objective function to be minimized, ``fun(x, *args) -> float``, where ``x`` is a 1-D array with shape ``(n,)`` and ``args`` is a tuple of the fixed parameters needed to completely specify the function. ``fun`` must support differentiation. x0: initial guess. Array of real elements of size ``(n,)``, where ``n`` is the number of independent variables. args: extra arguments passed to the objective function. method: solver type. Currently only ``"BFGS"`` is supported. tol: tolerance for termination. For detailed control, use solver-specific options. options: a dictionary of solver options. All methods accept the following generic options: - maxiter (int): Maximum number of iterations to perform. Depending on the method each iteration may use several function evaluations. Returns: An :class:`OptimizeResults` object. NzDargs argument to jax.scipy.optimize.minimize must be a tuple, got {}c|gS)Nr)rargsrs rzminimize..isCMDMrbfgs) rrrrrrrrrz'l-bfgs-experimental-do-not-rely-on-thiszMethod z not recognized) isinstancetuple TypeErrorformatlowerr convergedjnp logical_notfailedr x_krf_kg_kH_krngevkr ValueError) rx0r#methodrr msg fun_with_argsresultsrs ` ` rminimizer;5sSZ _G D% PC CJJt$ %%)- \\^vM299G#//'.."AAG W[[#*")..&{{&{{$+KK ' ' &yy ** \\^@@mR;7;G#//'.."AAG W[[#*")..&{{&{{$( ' ' &yy ** WVHO455r)r)rrr6r r#r'r7strrz float | Noner zMapping[str, Any] | Nonereturnr ) __future__rcollections.abcrrtypingrrjax._srcrr,jax._src.scipy.optimize.bfgsr jax._src.scipy.optimize._lbfgsr jax._src.typingr r r;rrrrEs#-"!6:!j<P6 (,P6 P6 P6 P6  P6  P6&P6P6r