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Everything about Subgradient method
Concept in convex optimization mathematics
Subgradient methods are convex optimization methods which use subderivatives. Originally developed by Naum Z. Shor and others in the 1960s and 1970s, subgradient methods are convergent when applied even to a non-differentiable objective function. When the objective function is differentiable, subgradient methods for unconstrained problems use the same search direction as the method of gradient descent. Wikipedia →
🧭 Start here: a 6-step reading path
- Read the overview: Subgradient methodA 5-minute orientation
- Skim a recent paper: Subgradient Extragradient Method with Double Inertial Steps for Variational InequalitiesInfluential work since 2022
- Pick up a classic book: Numerical Methods for Differential Equations, Optimization, and Technological ProblemsThe most-reprinted book on the subject
- Read the landmark paper: Modified subgradient extragradient method for variational inequality problemsCited 147 times
- Go sideways: Convex optimizationA closely related topic
- Go sideways: Frank–Wolfe algorithmA closely related topic
📖 Definitions
No dictionary entry for this exact phrase.
🗂️ The subject at a glance
🏆 Landmark papers (most cited)
Modified subgradient extragradient method for variational inequality problems
Stochastic Subgradient Method Converges on Tame Functions
An aggregate subgradient method for nonsmooth convex minimization
Distributed Subgradient Method With Edge-Based Event-Triggered Communication
A Proximal Bundle Method with Approximate Subgradient Linearizations
A Subgradient Method for Vector Optimization Problems
Convergence of a simple subgradient level method
Halpern subgradient extragradient method extended to equilibrium problems
🆕 Influential since 2022
Subgradient Extragradient Method with Double Inertial Steps for Variational Inequalities
Distributed Subgradient Method in Open Multiagent Systems
On Distributed Nonconvex Optimization: Projected Subgradient Method for Weakly Convex Problems in Networks
A Generalized Newton Method for Subgradient Systems
S-Iteration inertial subgradient extragradient method for variational inequality and fixed point problems
📚 Books worth reading
Numerical Methods for Differential Equations, Optimization, and Technological ProblemsSergey Repin · 2012
Mathematical Methodologies in Pattern Recognition and Machine LearningPedro Latorre Carmona · 2012
Linguistic structure predictionNoah A. Smith · 2011
Nonlinear analysis and optimizationB. Cornet, A. Auslender · 1987- 📕Conditional subgradient methods and ergodic convergence in nonsmooth optimizationAnn-Brith Strömberg · 1997
Ranked by number of editions on Open Library, a rough measure of how lasting a book is.
🔗 Related topics
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