Q:

Maximizing 1-norm: using binary variables to relax non-convexity

It is well-known that when we maximize a 1-norm, e.g., $\|Ax\|_1$, we can use binary variables and obtain a mixed-integer convex problem (otherwise maximizing 1-norm is non-convex). I am mentioning this in a report, but I need a reference. I checked YALMIP logical programming post on this page, it is relevant but not fully the same.
Do you know which source I can cite? I am using YALMIP to solve such a problem, and it automatically reformulates as explained.

A:

Provide the standard citation for YALMIP
@inproceedings{Lofberg2004,
address = {Taipei, Taiwan},
author = {L{\"{o}}fberg, J.},
booktitle = {In Proceedings of the CACSD Conference},
title = {YALMIP : A Toolbox for Modeling and Optimization in MATLAB},
year = {2004}
}

which is shown at https://yalmip.github.io/reference/lofberg2004/
Then perhaps you can also reference the relevant YALMIP wiki page for your problem, namely YALMIP Logics and integer-programming representations.

