A theory on the absence of spurious solutions for nonconvex and nonsmooth optimization

C. Josz, Y. Ouyang, R. Y. Zhang, J. Lavaei, S. Sojoudi

Research output: Contribution to journalConference articlepeer-review


We study the set of continuous functions that admit no spurious local optima (i.e. local minima that are not global minima) which we term global functions. 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 nonconvex and nonsmooth optimization problems arising in tensor decomposition applications are global functions. This is the first result concerning nonconvex methods for nonsmooth objective functions. Our result provides a theoretical guarantee for the widely-used `1 norm to avoid outliers in nonconvex optimization.

Original languageEnglish (US)
Pages (from-to)2441-2449
Number of pages9
JournalAdvances in Neural Information Processing Systems
StatePublished - Jan 1 2018
Externally publishedYes
Event32nd Conference on Neural Information Processing Systems, NeurIPS 2018 - Montreal, Canada
Duration: Dec 2 2018Dec 8 2018

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Information Systems
  • Signal Processing


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