Hello everyone,
I am exploring convex optimization techniques for a dataset where the values are non-additive (i.e., 1 + 1 ≠ 2). Traditional convex optimization methods often rely on the assumption of additivity, but this doesn’t hold true for my data.
Does anyone know of a convex optimization model or approach that can handle non-additive values effectively? Any references, algorithms, or personal experiences would be greatly appreciated.
Thanks!
It’s not clear at all what help you are seeking. Are you referring to nonlinear functions? There are many nonlinear functions and constraints which are compatible with convex optimization and are supported by CVX.
Nevertheless, this is not a statistics modeling forum, and people seeking help are expected to have already formulated a convex optimization model suited to their purpose.
If you want more insight as to convex optimization modeling in statistics, there sre several examples in Convex Optimization – Boyd and Vandenberghe