Distributed Optimization and Statistical Learning via the Alternating Direction Method of Multipliers
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Summary
It is argued that the alternating direction method of multipliers is well suited to distributed convex optimization, and in particular to large-scale problems arising in statistics, machine learning, and related areas.
- Type
- book
- Published
- 2011-05-23
- Cited by
- 6,041
- References
- 184
- OpenAlex
- https://openalex.org/W2164278908
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:51789432
Keywords
Computer science, Optimization problem, Convex optimization, Mathematical optimization, Regular polygon
References
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- Compressed Sensing
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- Éléments d'économie politique pure, ou, Théorie de la richesse sociale
- Fundamentals of Convex Analysis
- Convex Analysis And Nonlinear Optimization
- Probabilistic graphical models : principles and techniques
- Augmented Lagrangian methods : applications to the numerical solution of boundary-value problems
- Splitting methods for monotone operators with applications to parallel optimization
- Distributed Asynchronous Deterministic and Stochastic Gradient Optimization Algorithms
- Problems in decentralized decision making and computation
- Decomposition Method with a Variable Parameter for a Class of Monotone Variational Inequality Problems
- Alternating Direction Method with Self-Adaptive Penalty Parameters for Monotone Variational Inequalities
- Application of the alternating direction method of multipliers to separable convex programming problems
- A Review of Fast L(1)-Minimization Algorithms for Robust Face Recognition
- Opérateurs maximaux monotones et semi-groupes de contractions dans les espaces de Hilbert
- Hadoop: The Definitive Guide
- An Augmented Lagrangian Approach to Constrained MAP Inference
- Constrained Optimization and Lagrange Multiplier Methods
- Applied Numerical Linear Algebra
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- T2 Shuffling: Sharp, Multi-Contrast, Volumetric Fast Spin-Echo Imaging
- Mirror Prox Algorithm for Multi-Term Composite Minimization and Alternating Directions
- An Assessment of Iterative Reconstruction Methods for Sparse Ultrasound Imaging
- A Fast Algorithm for Convolutional Structured Low-rank Matrix Recovery
- Magnetic resonance fingerprinting: a technical review
- Splitting Algorithms for Convex Optimization and Applications to Sparse Matrix Factorization
- Stochastic Dual Coordinate Ascent with Alternating Direction Method of Multipliers
- A Statistical Learning Approach to Personalization in Revenue Management
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- A Novel Sparse Group Gaussian Graphical Model for Functional Connectivity Estimation
- Accelerated Stochastic Gradient Method for Composite Regularization
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- Doubly Regularized Portfolio with Risk Minimization
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