# tf.raw_ops.Minimum

Returns the min of x and y (i.e. x < y ? x : y) element-wise.

Both inputs are number-type tensors (except complex). `minimum` expects that both tensors have the same `dtype`.

#### Examples:

````x = tf.constant([0., 0., 0., 0.])`
`y = tf.constant([-5., -2., 0., 3.])`
`tf.math.minimum(x, y)`
`<tf.Tensor: shape=(4,), dtype=float32, numpy=array([-5., -2., 0., 0.], dtype=float32)>`
```

Note that `minimum` supports broadcast semantics.

````x = tf.constant([-5., 0., 0., 0.])`
`y = tf.constant([-3.])`
`tf.math.minimum(x, y)`
`<tf.Tensor: shape=(4,), dtype=float32, numpy=array([-5., -3., -3., -3.], dtype=float32)>`
```

If inputs are not tensors, they will be converted to tensors. See `tf.convert_to_tensor`.

````x = tf.constant([-3.], dtype=tf.float32)`
`tf.math.minimum([-5], x)`
`<tf.Tensor: shape=(1,), dtype=float32, numpy=array([-5.], dtype=float32)>`
```

`x` A `Tensor`. Must be one of the following types: `bfloat16`, `half`, `float32`, `float64`, `uint8`, `int16`, `int32`, `int64`.
`y` A `Tensor`. Must have the same type as `x`.
`name` A name for the operation (optional).

A `Tensor`. Has the same type as `x`.

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