Alternating direction method of multipliers for sparse principal component analysis

DOI10.1007/s40305-013-0016-9zbMath1336.62160arXiv1111.6703OpenAlexW2090214813MaRDI QIDQ457552

Shi-Qian Ma

Publication date: 29 September 2014

Published in: Journal of the Operations Research Society of China (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1111.6703

zbMATH Keywords

semidefinite programmingdeflationaugmented Lagrangian methodalternating direction methodprojection onto the simplexsparse PCA

Mathematics Subject Classification ID

Factor analysis and principal components; correspondence analysis (62H25) Classification and discrimination; cluster analysis (statistical aspects) (62H30) Numerical mathematical programming methods (65K05) Semidefinite programming (90C22) Convex programming (90C25)

Related Items (10)

An implementable splitting algorithm for the \(\ell_1\)-norm regularized split feasibility problem ⋮ Alternating proximal gradient method for convex minimization ⋮ Alternating direction method of multipliers for separable convex optimization of real functions in complex variables ⋮ Using ℓ1-Relaxation and Integer Programming to Obtain Dual Bounds for Sparse PCA ⋮ Solving sparse principal component analysis with global support ⋮ A homotopy alternating direction method of multipliers for linearly constrained separable convex optimization ⋮ An extragradient-based alternating direction method for convex minimization ⋮ Alternating direction method of multipliers for real and complex polynomial optimization models ⋮ Alternating direction method of multipliers for penalized zero-variance discriminant analysis ⋮ Proximal Gradient Method for Nonsmooth Optimization over the Stiefel Manifold

Uses Software

• SPGL1
• RecPF

Cites Work

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