Proximal Gradient Methods for Machine Learning and Imaging
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Publication:5028165
DOI10.1007/978-3-030-86664-8_4OpenAlexW4205862246MaRDI QIDQ5028165
Publication date: 8 February 2022
Published in: Harmonic and Applied Analysis (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1007/978-3-030-86664-8_4
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Cites Work
- Convex analysis and monotone operator theory in Hilbert spaces
- An inertial proximal method for maximal monotone operators via discretization of a nonlinear oscillator with damping
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- Nonlinear total variation based noise removal algorithms
- A Fast Iterative Shrinkage-Thresholding Algorithm for Linear Inverse Problems
- Parallel coordinate descent methods for big data optimization
- A mathematical introduction to compressive sensing
- Proximal alternating linearized minimization for nonconvex and nonsmooth problems
- Compositions and convex combinations of averaged nonexpansive operators
- On the convergence of the iterates of the ``fast iterative shrinkage/thresholding algorithm
- Dualization of signal recovery problems
- Pegasos: primal estimated sub-gradient solver for SVM
- Convex functions, monotone operators and differentiability.
- Über die Methode sukzessiver Approximationen
- On the convergence of the proximal algorithm for nonsmooth functions involving analytic features
- Ergodic convergence to a zero of the sum of monotone operators in Hilbert space
- Error bounds and convergence analysis of feasible descent methods: A general approach
- Image recovery via total variation minimization and related problems
- Introductory lectures on convex optimization. A basic course.
- An algorithm for total variation minimization and applications
- A fast dual proximal gradient algorithm for convex minimization and applications
- From error bounds to the complexity of first-order descent methods for convex functions
- Support-vector networks
- Convergence analysis of a proximal Gauss-Newton method
- Convergence of descent methods for semi-algebraic and tame problems: proximal algorithms, forward-backward splitting, and regularized Gauss-Seidel methods
- Global error bounds for piecewise convex polynomials
- Convergence of stochastic proximal gradient algorithm
- Coordinate descent algorithms
- Splitting methods with variable metric for Kurdyka-Łojasiewicz functions and general convergence rates
- Random block coordinate descent methods for linearly constrained optimization over networks
- Fast convergence of inertial dynamics and algorithms with asymptotic vanishing viscosity
- Construction of fixed points of nonlinear mappings in Hilbert space
- Methodes itératives pour les équations et inéquations aux dérivées partielles non linéaires de type monotone. (Iteration methods for nonlinear equations and inequations with partial derivatives of monotone type)
- A note on segmenting Mann iterates
- A Differential Equation for Modeling Nesterov's Accelerated Gradient Method: Theory and Insights
- Proximal Splitting Methods in Signal Processing
- Optimization with Sparsity-Inducing Penalties
- Efficiency of Coordinate Descent Methods on Huge-Scale Optimization Problems
- Nonparametric sparsity and regularization
- Convex Optimization in Normed Spaces
- Convexity and Optimization in Banach Spaces
- Weak Sharp Minima in Mathematical Programming
- Proximal Alternating Minimization and Projection Methods for Nonconvex Problems: An Approach Based on the Kurdyka-Łojasiewicz Inequality
- Asymptotic Convergence Analysis of the Proximal Point Algorithm
- Parallel Random Coordinate Descent Method for Composite Minimization: Convergence Analysis and Error Bounds
- Support Vector Machines
- Clarke Subgradients of Stratifiable Functions
- Proximal Thresholding Algorithm for Minimization over Orthonormal Bases
- Bregmanized Nonlocal Regularization for Deconvolution and Sparse Reconstruction
- A forward–backward splitting algorithm for the minimization of non-smooth convex functionals in Banach space
- Robust Stochastic Approximation Approach to Stochastic Programming
- Splitting Algorithms for the Sum of Two Nonlinear Operators
- Applications of a Splitting Algorithm to Decomposition in Convex Programming and Variational Inequalities
- New Proximal Point Algorithms for Convex Minimization
- Monotone Operators and the Proximal Point Algorithm
- A Nonlinear Primal-Dual Method for Total Variation-Based Image Restoration
- Optimization Methods for Large-Scale Machine Learning
- An iterative thresholding algorithm for linear inverse problems with a sparsity constraint
- Error Bounds for Piecewise Convex Quadratic Programs and Applications
- Optimal Convergence Rates for Nesterov Acceleration
- On perturbed proximal gradient algorithms
- The Variable Metric Forward-Backward Splitting Algorithm Under Mild Differentiability Assumptions
- Fast Gradient-Based Algorithms for Constrained Total Variation Image Denoising and Deblurring Problems
- Stochastic Dual Coordinate Ascent Methods for Regularized Loss Minimization
- The Łojasiewicz Inequality for Nonsmooth Subanalytic Functions with Applications to Subgradient Dynamical Systems
- A Convergent Incremental Gradient Method with a Constant Step Size
- Signal Recovery by Proximal Forward-Backward Splitting
- Stochastic Quasi-Fejér Block-Coordinate Fixed Point Iterations with Random Sweeping
- Proximité et dualité dans un espace hilbertien
- The solution by iteration of nonlinear functional equations in Banach spaces
- Convex programming in Hilbert space
- Weak convergence of the sequence of successive approximations for nonexpansive mappings
- Convex Analysis
- On the Mann Iterative Process
- An Iterative Regularization Method for Total Variation-Based Image Restoration
- An introduction to continuous optimization for imaging
- Stochastic Estimation of the Maximum of a Regression Function
- Mean Value Methods in Iteration
- A Stochastic Approximation Method
