A theory on the absence of spurious solutions for nonconvex and nonsmooth optimization
Josz, Cedric, Ouyang, Yi, Zhang, Richard, Lavaei, Javad, Sojoudi, Somayeh
–Neural Information Processing Systems
We study the set of continuous functions that admit no spurious local optima (i.e. They satisfy various powerful properties for analyzing nonconvex and nonsmooth optimization problems. For instance, they satisfy a theorem akin to the fundamental uniform limit theorem in the analysis regarding continuous functions. Global functions are also endowed with useful properties regarding the composition of functions and change of variables. Using these new results, we show that a class of non-differentiable nonconvex optimization problems arising in tensor decomposition applications are global functions.
Neural Information Processing Systems
Feb-14-2020, 10:42:42 GMT