Fixed-Point Algorithms for Inverse Problems in Science and Engineering

399 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Proximal Markov chain Monte Carlo algorithms (2013, background): “…scenarios, and are now used extensively in the proximal optimisation algorithms that underpinmodern high-dimensional statistics, signal and image processing, andmachine learning (Agarwal et al. 2012; Chandrasekaran and Jordan 2013; Combettes and Pesquet 2011; Parikh and Boyd 2014).”
Asynchronous distributed optimization using a randomized alternating direction method of multipliers (2013, methodology): “This paper focuses on a third class of methods derived from proximal splitting methods [8], [9], [10].”
From Local SGD to Local Fixed Point Methods for Federated Learning (2020, background): “Indeed, most, if not all, iterative methods are fixed-point methods, which aim at finding a fixed point of some operator (Bauschke et al., 2011; Condat et al., 2019).”
Optimization with Sparsity-Inducing Penalties (2011, background): “Note that, unlike for the simple proximal scheme, we cannot guarantee that the sequence of iterates generated by the accelerated version is itself convergent [38].”
Sparse Modeling for Image and Vision Processing (2014, methodology): “For example, proximal splitting techniques [Combettes and Pesquet, 2011, Chambolle and Pock, 2011, Condat, 2013, Raguet et al., 2013], such as the alternating direction method of multipliers [see Boyd et al., 2011], are often used in signal processing.”