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Everything about Proximal gradient method
Form of projection
Proximal gradient methods are a generalized form of projection used to solve non-differentiable convex optimization problems. Wikipedia →
🧭 Start here: a 6-step reading path
- Read the overview: Proximal gradient methodA 5-minute orientation
- Skim a recent paper: An accelerated proximal gradient method for multiobjective optimizationInfluential work since 2022
- Pick up a classic book: Statistical Atlases and Computational Models of the Heart. Imaging and Modelling ChallengesThe most-reprinted book on the subject
- Read the landmark paper: A Proximal Stochastic Gradient Method with Progressive Variance ReductionCited 325 times
- Go sideways: Landweber iterationA closely related topic
- Go sideways: Proximal operatorA closely related topic
📖 Definitions
No dictionary entry for this exact phrase.
🗂️ The subject at a glance
🏆 Landmark papers (most cited)
A Proximal Stochastic Gradient Method with Progressive Variance Reduction
Smoothing proximal gradient method for general structured sparse regression
Proximal Gradient Method for Nonsmooth Optimization over the Stiefel Manifold
Alternating proximal gradient method for sparse nonnegative Tucker decomposition
A Proximal-Gradient Homotopy Method for the Sparse Least-Squares Problem
Riemannian proximal gradient methods
Alternating Proximal Gradient Method for Convex Minimization
An accelerated dual proximal gradient method for applications in viscoplasticity
🆕 Influential since 2022
An accelerated proximal gradient method for multiobjective optimization
Convergence rates analysis of a multiobjective proximal gradient method
A Newton-type proximal gradient method for nonlinear multi-objective optimization problems
Sparse identification of Lagrangian for nonlinear dynamical systems via proximal gradient method
Momentum-Based Variance-Reduced Proximal Stochastic Gradient Method for Composite Nonconvex Stochastic Optimization
📚 Books worth reading
Statistical Atlases and Computational Models of the Heart. Imaging and Modelling ChallengesOscar Camara · 2012
The Elements of Statistical LearningTrevor Hastie, Robert Tibshirani · 2001
Molecular diagnosticsGeorge P. Patrinos, Wilhelm Ansorge · 2009
Functional Analysis, Calculus of Variations and Optimal ControlFrancis Clarke · 2013
Ranked by number of editions on Open Library, a rough measure of how lasting a book is.
🔗 Related topics
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