Data-Driven Nonsmooth Optimization
From MaRDI portal
Publication:5210515
DOI10.1137/18M1207685zbMath1435.90105arXiv1808.00946OpenAlexW3099853293WikidataQ126402908 ScholiaQ126402908MaRDI QIDQ5210515
Axel Ringh, Ozan Öktem, Johan Karlsson, Sebastian Banert, Jonas Adler
Publication date: 21 January 2020
Published in: SIAM Journal on Optimization (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/1808.00946
convex optimizationinverse problemscomputerized tomographymonotone operatorsmachine learningproximal algorithms
Convex programming (90C25) Monotone operators and generalizations (47H05) General topics in artificial intelligence (68T01)
Related Items (10)
Multivariate Monotone Inclusions in Saddle Form ⋮ Preconditioned three-operator splitting algorithm with applications to image restoration ⋮ Warped proximal iterations for monotone inclusions ⋮ Tutorial on Amortized Optimization ⋮ Invertible residual networks in the context of regularization theory for linear inverse problems ⋮ An inertial primal‐dual fixed point algorithm for composite optimization problems ⋮ Data-Driven Mirror Descent with Input-Convex Neural Networks ⋮ Randomized smoothing variance reduction method for large-scale non-smooth convex optimization ⋮ Solving inverse problems using data-driven models ⋮ Learning Maximally Monotone Operators for Image Recovery
Uses Software
Cites Work
- Unnamed Item
- Unnamed Item
- Distributed Optimization and Statistical Learning via the Alternating Direction Method of Multipliers
- A Fast Iterative Shrinkage-Thresholding Algorithm for Linear Inverse Problems
- Optimized first-order methods for smooth convex minimization
- Primal-dual splitting algorithm for solving inclusions with mixtures of composite, Lipschitzian, and parallel-sum type monotone operators
- Smooth strongly convex interpolation and exact worst-case performance of first-order methods
- Examples of discontinuous maximal monotone linear operators and the solution to a recent problem posed by B.F. Svaiter
- On the Douglas-Rachford splitting method and the proximal point algorithm for maximal monotone operators
- Statistical and computational inverse problems.
- A first-order primal-dual algorithm for convex problems with applications to imaging
- Deep neural network structures solving variational inequalities
- Performance of first-order methods for smooth convex minimization: a novel approach
- Asymmetric forward-backward-adjoint splitting for solving monotone inclusions involving three operators
- The Mathematics of Computerized Tomography
- Mathematical Methods in Image Reconstruction
- Mathematics of Electron Tomography
- Proximal Splitting Methods in Signal Processing
- Convergence Analysis of Primal-Dual Algorithms for a Saddle-Point Problem: From Contraction Perspective
- Convexity and Optimization in Banach Spaces
- A Monotone+Skew Splitting Model for Composite Monotone Inclusions in Duality
- Easily Parallelizable and Distributable Class of Algorithms for Structured Sparsity, with Optimal Acceleration
- From Sparse Solutions of Systems of Equations to Sparse Modeling of Signals and Images
- Monotone Operators and the Proximal Point Algorithm
- Solving ill-posed inverse problems using iterative deep neural networks
- On the convergence rate of a forward-backward type primal-dual splitting algorithm for convex optimization problems
- Deep unfolding of a proximal interior point method for image restoration
- Solving Coupled Composite Monotone Inclusions by Successive Fejér Approximations of their Kuhn--Tucker Set
- A Douglas--Rachford Type Primal-Dual Method for Solving Inclusions with Mixtures of Composite and Parallel-Sum Type Monotone Operators
- Proximité et dualité dans un espace hilbertien
- Convex analysis and monotone operator theory in Hilbert spaces
This page was built for publication: Data-Driven Nonsmooth Optimization
