State-Dependent Lyapunov Analysis of Rank-1 Matrix Factorization
Jaehong Moon
Sep 2026
Machine Learning
Abstract
We develop a state-dependent Lyapunov framework for gradient descent on rank-1 matrix factorization. A parameterized quadratic certificate $I(\delta;\,\cdot)$ generates strictly nested sublevel sets. Their ordering assigns each point a state $\delta$, while a boundary-inward property makes this state monotone along gradient-descent trajectories. Together with internal chain transitivity, this geometry identifies the limiting dynamics even when all relevant stationary points are unstable. For scalar and rank-1 factorization, it yields convergence to a global minimizer below the stability threshold and to a balanced period-$2$ orbit in a post-critical interval, for almost every initialization in an explicit region.
We formalize the mechanism through structural and dynamical axioms. Within the origin-centered, exchange-symmetric quadratic class, the axioms determine the ordered level-set geometry uniquely up to a monotone relabeling of the state, recovering the scalar geometry identified by Liang--Mont{\'u}far. Additional analytic and numerical examples suggest broader applicability.
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