VpfsddlmZddlmZmZddlmZddlZddlm Z ddl m Z ddlm Z ddl mZGdd e Z dddd ddZdS)) annotations)CallableMapping)AnyN) minimize_bfgs)_minimize_lbfgs) NamedTuplecneZdZUdZded<ded<ded<ded<ded <d ed <ded <ded <ded<dS)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. jax.Arrayxzbool | jax.Arraysuccesszint | jax.Arraystatusfunjaczjax.Array | Nonehess_invnfevnjevnitN)__name__ __module__ __qualname____doc____annotations__`/var/www/html/nettyfy-visnx/env/lib/python3.11/site-packages/jax/_src/scipy/optimize/minimize.pyr r s,,,......rr r)toloptionsrrx0r argstuplemethodstrr float | NonerMapping[str, Any] | Nonereturnc |i}tts$d}t|fd}|dkrot ||fi|}|jtj|j z} t|j | |j |j |j|j|j|j|j S|dkrjt'||fi|}|jtj|j z} 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|gRS)Nr)r r!rs rzminimize..jsCCMDMMMrbfgs) r rrrrrrrrz'l-bfgs-experimental-do-not-rely-on-thiszMethod z not recognized) isinstancer" TypeErrorformatlowerr convergedjnp logical_notfailedr x_krf_kg_kH_krngevkr ValueError) rr r!r#rrmsg fun_with_argsresultsrs ` ` rminimizer>6sxZ _G D% & PC CJJt$$ % %%)))))- \\^^vM29999G#/'."A"AAG W[#*").&{&{$+K ' ' &y * * ** \\^^@@@mR;;7;;G#/'."A"AAG W[#*").&{&{$( ' ' &y * * ** 4V444555r)r)rrr r r!r"r#r$rr%rr&r'r ) __future__rcollections.abcrrtypingrjaxjax._src.scipy.optimize.bfgsrjax._src.scipy.optimize._lbfgsrr jax.numpynumpyr1r r>rrrrGs#"""""-------- 666666::::::j<P6 (,P6P6P6P6P6P6P6P6r