# jax.numpy.fmin¶

jax.numpy.fmin(x1, x2)[source]

Element-wise minimum of array elements.

LAX-backend implementation of fmin(). Original docstring below.

fmin(x1, x2, /, out=None, *, where=True, casting=’same_kind’, order=’K’, dtype=None, subok=True[, signature, extobj])

Compare two arrays and returns a new array containing the element-wise minima. If one of the elements being compared is a NaN, then the non-nan element is returned. If both elements are NaNs then the first is returned. The latter distinction is important for complex NaNs, which are defined as at least one of the real or imaginary parts being a NaN. The net effect is that NaNs are ignored when possible.

Parameters

x2 (x1,) – The arrays holding the elements to be compared. If x1.shape != x2.shape, they must be broadcastable to a common shape (which becomes the shape of the output).

Returns

y – The minimum of x1 and x2, element-wise. This is a scalar if both x1 and x2 are scalars.

Return type

ndarray or scalar

fmax()

Element-wise maximum of two arrays, ignores NaNs.

minimum()

Element-wise minimum of two arrays, propagates NaNs.

amin()

The minimum value of an array along a given axis, propagates NaNs.

nanmin()

The minimum value of an array along a given axis, ignores NaNs.

Notes

New in version 1.3.0.

The fmin is equivalent to np.where(x1 <= x2, x1, x2) when neither x1 nor x2 are NaNs, but it is faster and does proper broadcasting.

Examples

>>> np.fmin([2, 3, 4], [1, 5, 2])
array([1, 3, 2])

>>> np.fmin(np.eye(2), [0.5, 2])
array([[ 0.5,  0. ],
[ 0. ,  1. ]])

>>> np.fmin([np.nan, 0, np.nan],[0, np.nan, np.nan])
array([ 0.,  0., nan])