Everything about Random coordinate descent
Optimization algorithm
Randomized (Block) Coordinate Descent Method is an optimization algorithm popularized by Nesterov (2010) and Richtárik and Takáč (2011). The first analysis of this method, when applied to the problem of minimizing a smooth convex function, was performed by Nesterov (2010). In Nesterov's analysis the method needs to be applied to a quadratic perturbation of the original function with an unknown scaling factor. Richtárik and Takáč (2011) provide iteration complexity bounds that do not require this assumption, meaning the method is applied directly to the objective function. Additionally, they generalize the framework to the problem of minimizing a composite function, specifically the sum of a smooth convex function and a convex block-separable function. Wikipedia →
🧭 Start here: a 6-step reading path
- Read the overview: Random coordinate descentA 5-minute orientation
- Skim a recent paper: Random coordinate descent: A simple alternative for optimizing parameterized quantum circuitsInfluential work since 2022
- Read the landmark paper: Random Coordinate Descent Algorithms for Multi-Agent Convex Optimization Over NetworksCited 85 times
- Go sideways: Mirror descentA closely related topic
- Go sideways: Stream functionA closely related topic
📖 Definitions
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🗂️ The subject at a glance
🏆 Landmark papers (most cited)
Random Coordinate Descent Algorithms for Multi-Agent Convex Optimization Over Networks
A random coordinate descent algorithm for optimization problems with composite objective function and linear coupled constraints
Efficient random coordinate descent algorithms for large-scale structured nonconvex optimization
Parallel Random Coordinate Descent Method for Composite Minimization: Convergence Analysis and Error Bounds
Random Block Coordinate Descent Methods for Linearly Constrained Optimization over Networks
Random Coordinate Descent with Spinor-matrices and Geometric Filters for Efficient Loop Closure
Random Coordinate Descent for Resource Allocation in Open Multiagent Systems
On the Efficiency of Random Permutation for ADMM and Coordinate Descent
🆕 Influential since 2022
Random coordinate descent: A simple alternative for optimizing parameterized quantum circuits
Random Coordinate Descent Methods for Nonseparable Composite Optimization
Accelerated Randomized Coordinate Descent for Solving Linear Systems
Linear Convergence of Random Dual Coordinate Descent on Nonpolyhedral Convex Problems
A novel block‐coordinate gradient descent algorithm for simultaneous grouped selection of fixed and random effects in joint modeling
📚 Books worth reading
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