The l1 norm, also known as the Manhattan norm or taxicab norm, measures the distance between two points in a space by summing the absolute differences of their coordinates. It is widely used in various fields, including optimization and machine learning, as it provides a way to quantify how 'far apart' two vectors are in a linear space. This norm emphasizes sparse solutions, which can be particularly beneficial when approximating functions with rational numbers.
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