Efficient algorithms for implementing incremental proximal-point methods
From MaRDI portal
Publication:6645949
DOI10.1007/s12532-024-00258-8MaRDI QIDQ6645949
Publication date: 29 November 2024
Published in: Mathematical Programming Computation (Search for Journal in Brave)
Cites Work
- Unnamed Item
- Unnamed Item
- A Fast Iterative Shrinkage-Thresholding Algorithm for Linear Inverse Problems
- On the ergodic convergence rates of a first-order primal-dual algorithm
- Incremental proximal methods for large scale convex optimization
- Exponentiated gradient versus gradient descent for linear predictors
- Ergodic convergence to a zero of the sum of monotone operators in Hilbert space
- Support-vector networks
- A first-order primal-dual algorithm for convex problems with applications to imaging
- Efficiency of minimizing compositions of convex functions and smooth maps
- An accelerated variance reducing stochastic method with Douglas-Rachford splitting
- Efficient algorithms for online decision problems
- CVXPY: A Python-Embedded Modeling Language for Convex Optimization
- The Proximal Average: Basic Theory
- Stochastic Methods for Composite and Weakly Convex Optimization Problems
- First-Order Methods in Optimization
- Stochastic Model-Based Minimization of Weakly Convex Functions
- The importance of better models in stochastic optimization
- Stochastic (Approximate) Proximal Point Methods: Convergence, Optimality, and Adaptivity
- Proximité et dualité dans un espace hilbertien
- Convex Analysis
- A Stochastic Approximation Method
This page was built for publication: Efficient algorithms for implementing incremental proximal-point methods
