Matrix Completion and Low-Rank Recovery
Nuclear-norm minimization and noisy matrix completion.
Matrix Completion and Low-Rank Recovery. Nuclear-norm minimization and noisy matrix completion.
Recent technical contributions
A handful of recent papers carry the methodological frontier of matrix completion and low-rank recovery forward. Exact matrix completion via convex optimization (Candes et al., 2009) is a primary reference for this area and develops new techniques or results that downstream work builds on.
Open methodological questions for matrix completion and low-rank recovery include sharpening the bridges between foundational theory and computational practice, extending classical results to broader or more structured settings, and integrating the techniques surveyed above with adjacent mathematical disciplines. The references listed in this page are the entry points that current work builds on.
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