SOURCE-LINKED INTELLIGENCE

Tight Majorizations and Convergence Rates of Nuclear Norm Minimization IRLS

arXiv · AI, language, vision and robotics · article · Aug 24, 2026 · UTC

Iteratively reweighted least squares (IRLS) methods constitute a natural approach to nuclear norm minimization, but their convergence rates and the role of the weight operator have remained poorly understood. This paper establishes sharp convergence rates for IRLS methods for constrained nuclear norm minimization in low-rank recovery. A central ingredient is a new majorization analysis for the smoothed nuclear norm: we prove that the harmonic-mean weight operator defines a valid global quadratic majorizer. Furthermore, we show that this weight operator is optimal within the family of power-mea

Read original source ↗ Open in workspace

recordType

paper

region

Global