A multi-stage convex relaxation approach to noisy structured low-rank matrix recovery
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Publication:2220914
DOI10.1007/s12532-020-00177-4zbMath1452.90258arXiv1703.03898OpenAlexW3003707928MaRDI QIDQ2220914
Shujun Bi, Shaohua Pan, Defeng Sun
Publication date: 25 January 2021
Published in: Mathematical Programming Computation (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/1703.03898
Complementarity and equilibrium problems and variational inequalities (finite dimensions) (aspects of mathematical programming) (90C33) Combinatorial optimization (90C27) Numerical methods of relaxation type (49M20)
Related Items (4)
Mixed-Projection Conic Optimization: A New Paradigm for Modeling Rank Constraints ⋮ A block symmetric Gauss-Seidel decomposition theorem for convex composite quadratic programming and its applications ⋮ MSCRA_rankmin ⋮ Optimality conditions for rank-constrained matrix optimization
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