Everything about Proximal gradient method

🧭 Start here: a 6-step reading path

1. Read the overview: Proximal gradient methodA 5-minute orientation
2. Skim a recent paper: Automatic Compression of Neural Network with Deep Reinforcement Learning Based on Proximal Gradient MethodInfluential work since 2022
3. Pick up a classic book: Statistical Atlases and Computational Models of the Heart. Imaging and Modelling ChallengesThe most-reprinted book on the subject
4. Read the landmark paper: Proximal Gradient Methods for General Smooth Graph Total Variation Model in Unsupervised LearningCited 6 times
5. Go sideways: Landweber iterationA closely related topic
6. Go sideways: Proximal operatorA closely related topic

🏆 Landmark papers (most cited)

• Proximal Gradient Methods for General Smooth Graph Total Variation Model in Unsupervised Learning

  Baochen Sun, Huibin Chang · 2022 · Journal of Scientific Computing · 6 citations

• IPGM: Inertial Proximal Gradient Method for Convolutional Dictionary Learning

  Jing Li, Xiao Wei, Fengpin Wang · 2021 · Electronics · 2 citations

🆕 Influential since 2022

• Automatic Compression of Neural Network with Deep Reinforcement Learning Based on Proximal Gradient Method

  Mingyi Wang, Jianhao Tang, Haoli Zhao · 2023 · Mathematics · 3 citations

📚 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

Ranked by number of editions on Open Library, a rough measure of how lasting a book is.

🔗 Related topics

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• Dykstra's projection algorithm Dykstra's algorithm is a method that computes a point in the intersection of convex sets, and is a variant of the alternating projection method (also called…
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• Projections onto convex sets In mathematics, projections onto convex sets (POCS), sometimes known as the alternating projection method, is a method to find a point in the intersection…
• Mirror descent In mathematics, mirror descent is an iterative optimization algorithm for finding a local minimum of a differentiable function. It generalizes algorithms…
• Gradient descent Gradient descent is a method for unconstrained mathematical optimization. It is a first-order iterative algorithm for minimizing a differentiable multivariate…