Binno: a 1st-order method for Bi-level nonconvex nonsmooth optimization for matrix factorizations

Abstract

In this work, we develop a method for nonconvex, nonsmooth bi-level optimization and we introduce Binno, a first order method that leverages proximal constructions together with carefully designed descent conditions and variational analysis. Within this framework, Binno provably enforces a descent property for the overall objective surrogate associated with the bi-level problem. Each iteration performs blockwise proximal-gradient updates for the upper and the lower problems separately and then forms a calibrated, block-diagonal convex combination of the two tentative iterates. A linesearch selects the combination weights to enforce simultaneous descent of both level-wise objectives, and we establish conditions guaranteeing the existence of such weights together with descent directions induced by the associated proximal-gradient maps. We also apply Binno in the context of sparse low-rank factorization, where the upper level uses elementwise $\ell_1$ penalties and the lower level uses nuclear norms, coupled via a Frobenius data term. We test Binno on synthetic matrix and a real traffic-video dataset, attaining lower relative reconstruction error and higher peak signal-to-noise ratio than some standard methods.

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ORCID for Andersen Ang: orcid.org/0000-0002-8330-758X

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