mindspore.scipy.optimize.minimize

mindspore.scipy.optimize.minimize(func, x0, args=(), *, method, tol=None, options=None)[source]

Minimization of scalar function of one or more variables.

This API for this function matches SciPy with some minor deviations:

  • Gradients of func are calculated automatically using MindSpore’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.

Note

  • minimize does not yet support differentiation or arguments in the form of multi-dimensional Tensor, but support for both is planned.

  • minimize is not supported on Windows platform yet.

Parameters

  • func (Callable) – 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 (Tensor) – initial guess. Array of real elements of size \((n,)\), where n is the number of independent variables.

  • args (Tuple) – extra arguments passed to the objective function. Default: ().

  • method (str) – solver type. Currently only “BFGS” is supported.

  • tol (float, optional) – tolerance for termination. For detailed control, use solver-specific options. Default: None.

  • options (Mapping[str, Any], optional) –

    a dictionary of solver options. All methods accept the following generic options, Default: None.

    • maxiter (int): Maximum number of iterations to perform. Depending on the method each iteration may use several function evaluations.

Returns

OptimizeResults, object holding optimization results.

Supported Platforms:

CPU GPU

Examples

>>> import numpy as onp
>>> from mindspore.scipy.optimize import minimize
>>> from mindspore.common import Tensor
>>> x0 = Tensor(onp.zeros(2).astype(onp.float32))
>>> def func(p):
>>>     x, y = p
>>>     return (x ** 2 + y - 11.) ** 2 + (x + y ** 2 - 7.) ** 2
>>> res = minimize(func, x0, method='BFGS', options=dict(maxiter=None, gtol=1e-6))
>>> print(res.x)
[3. 2.]