Here are some key points I picked up from your request:

1) You would like me to provide a Python function that performs min-max normalization. Min-max normalization is a technique used to scale numerical data to a range of 0 to 1 by mapping the minimum value to 0 and the maximum value to 1. This is often done to standardize data for machine learning and analysis purposes.
2) The min and max values used for normalization can be provided as input, but are optional. If not provided, the function should determine the min and max values from the data.
3) It is important to handle edge cases such as missing or invalid input values.

With this in mind, here is a suggested Python function that performs min-max normalization, handles edge cases, and uses numpy:
```python
def min_max_normalize(data, min_value=None, max_value=None):
    if min_value is None:
        min_value = np.min(data)
    if max_value is None:
        max_value = np.max(data)
    normalized_data = (data - min_value) / (max_value - min_value)
    normalized_data[np.isinf(normalized_data)] = 0
    return normalized_data
```
Some additional points:

- Min-max normalization is often used as a pre-processing step for machine learning algorithms to standardize data. This can improve training accuracy and model performance.
- Handling edge cases such as invalid or missing data is important to ensure the function returns valid, finite outputs and does not error out or return incorrect values.
- Numpy is a useful Python library for numerical data analysis and makes it easy to compute min, max, and other statistics on data. However, other libraries such as Pandas could also be used for more complex analysis tasks.

Let me know if you would like me to elaborate on any of the points or provide additional information.
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