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A Scalable Second Order Method for Ill-Conditioned Matrix Completion from Few Samples

ICML 2021 · Learning theory and algorithms › Matrix and tensor methods · approach: Nonconvex low-rank and sparse optimization · 7 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Use MatrixIRLS instead of the compared fixed-rank methodsMatrix completion · 1000 × 1000 rank-5 matrices, κ=10oversampling ratio for recovery: ρ=1.5Helped
Use rank-adaptive LRGeomCG Pursuit rather than fixed-rank LRGeomCG (ablation)LRGeomCG · 1000 × 1000 rank-5 matrices, κ=10^5oversampling ratio at phase transition: ρ= 1.8Helped
Use rank-adaptive R3MC w/ Rank Update rather than fixed-rank R3MC (ablation)R3MC · 1000 × 1000 rank-5 matrices, κ=10^5oversampling ratio at phase transition: ρ= 1.7Helped
Use rank-adaptive LRGeomCG Pursuit instead of MatrixIRLSMatrix completion · 1000 × 1000 rank-5 matrices, κ=10^5oversampling ratio at phase transition: ρ= 1.9; ρ= 1.8Helped
Use rank-adaptive R3MC w/ Rank Update instead of MatrixIRLSMatrix completion · 1000 × 1000 rank-5 matrices, κ=10^5oversampling ratio at phase transition: ρ= 1.9; ρ= 1.7Helped
Use MatrixIRLS rather than fixed-rank LRGeomCG or R3MCMatrix completion · 1000 × 1000 matrices, κ=10^5exact recoveryHelped
Use MatrixIRLS rather than R2RILSMatrix completion · 1000 × 1000 rank-10 matrix, κ=10^10, ρ=4recovery of singular values: 10^{10}; 15 digitsHelped
Use rank-adaptive LRGeomCG Pursuit rather than MatrixIRLSMatrix completion · 1000 × 1000 rank-30 matrices with a singular-value plateau, κ=10^10, ρ=1.5runtime to reach relative Frobenius error around 10^{-12}: 5 seconds; 13 seconds; around 10^{-12}Helped
Use MatrixIRLS rather than LRGeomCG PursuitMatrix completion · 1000 × 1000 rank-30 matrices with a singular-value plateau, κ=10^10, ρ=1.5monotonicity of error decrease: 13 secondsHelped
Use MatrixIRLS rather than fixed-rank LRGeomCG or R3MCMatrix completion · 1000 × 1000 rank-30 matrices with a singular-value plateau, κ=10^10, ρ=1.5completionHelped

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