Low Rank Tensor Completion via Adaptive ADMM
We consider a novel algorithm, for the completion of partially observed low-rank tensors, as a generalization of matrix completion. The proposed low-rank tensor completion (TC) method builds on the conventional nuclear norm (NN) minimization-based low-rank TC paradigm, by leveraging the alternating direction method of multipliers (ADMM) optimization framework. To that extend the original NN minimization problem is reformulated into multiple subproblems, which are then solved iteratively via closed-form proximal operators, making use of over-relaxation and an adaptive penalty parameter update scheme, to further speed up convergence and improve the overall performance of the method. Simulat...
| Authors | Niclas Führling, Getuar Rexhepi, Giuseppe Thadeu Freitas de Abreu |
|---|---|
| Primary category | stat.ML |
| Published | 5 May 2026 |
| Last updated at source | 5 May 2026 |
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