Question: Under what conditions might I choose the absolute error as the basis for a loss function, instead of squared error? If I was trying to

Under what conditions might I choose the absolute error as the basis for a loss function, instead of squared error?
If I was trying to implement a learning algorithm using a machine learning software like Scikit-Learn and I am worried about computation performance.
If I was using gradient descent instead of other methods
If I was solving a problem that was very large in terms of the number of dimensions.
If I was really worried about outliers having an out-sized effect on the model..
Under what conditions might I choose the absolute

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