*Last updated: 2023-03-16.*
# tf_quant_finance.math.qmc.sobol_generating_matrices
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Returns Sobol generating matrices.
```python
tf_quant_finance.math.qmc.sobol_generating_matrices(
dim, num_results, num_digits, validate_args=False, dtype=None, name=None
)
```
#### Examples
```python
import tf_quant_finance as tff
# Example: Creating the 4D Sobol generating matrices.
dim = 4
num_results = 500
num_digits = 9
tff.math.qmc.sobol_generating_matrices(dim, num_results, num_digits)
# ==> tf.Tensor([
# [256, 128, 64, 32, 16, 8, 4, 2, 1],
# [256, 384, 320, 480, 272, 408, 340, 510, 257],
# [256, 384, 192, 288, 464, 184, 284, 394, 209],
# [256, 384, 64, 160, 496, 232, 324, 294, 433],
# ], shape=(4, 9), dtype=int32)
```
#### Args:
* `dim`: Positive scalar `Tensor` of integers with rank 0. The event size of
points which can be sampled from the resulting generating matrices.
* `num_results`: Positive scalar `Tensor` of integers with rank 0. The maximum
number of points which can be sampled from the resulting generating
matrices.
* `num_digits`: Positive scalar `Tensor` of integers with rank 0. The base-2
precision of points which can be sampled from the resulting generating
matrices.
* `validate_args`: Python `bool` indicating whether to validate arguments.
Default value: `False`.
* `dtype`: Optional `dtype`. The `dtype` of the output `Tensor` (either a signed
or unsigned integer `dtype`).
Default value: `None` which maps to `int32`.
* `name`: Python `str` name prefixed to ops created by this function.
Default value: `None` which maps to `sobol_generating_matrices`.
#### Returns:
A scalar `Tensor` with shape `(dim, log_num_results)` where
`log_num_results = ceil(log2(num_results))`.