ConstrainedQuadraticModel.iter_violations(sample_like, *, skip_satisfied: bool = False, clip: bool = False) Iterator[Tuple[Hashable, Union[int, float, numpy.number]]][source]

Yield violations for all constraints.

Parameters
• sample_like – A sample over the CQM variables.

• skip_satisfied – If True, does not yield constraints that are satisfied.

• clip – If True, negative violations are rounded up to 0.

Yields

A 2-tuple containing the constraint label and the amount of constraints violation.

Example

```>>> i, j, k = dimod.Binaries(['i', 'j', 'k'])
>>> cqm.add_constraint(i + j + k == 10, label='equal')
'equal'
>>> cqm.add_constraint(i + j <= 15, label='less equal')
'less equal'
>>> cqm.add_constraint(j - k >= 0, label='greater equal')
'greater equal'
```

Check the violations of a sample that satisfies all constraints.

```>>> sample = {'i': 3, 'j': 5, 'k': 2}
>>> for label, violation in cqm.iter_violations(sample, clip=True):
...     print(label, violation)
equal 0.0
less equal 0.0
greater equal 0.0
```

Check the violations for a sample that does not satisfy all of the constraints.

```>>> sample = {'i': 3, 'j': 2, 'k': 5}
>>> for label, violation in cqm.iter_violations(sample, clip=True):
...     print(label, violation)
equal 0.0
less equal 0.0
greater equal 3.0
```
```>>> sample = {'i': 3, 'j': 2, 'k': 5}
>>> for label, violation in cqm.iter_violations(sample, skip_satisfied=True):
...     print(label, violation)
greater equal 3.0
```