If you use RegularizedProblems.jl in your work, please cite using the format given in CITATION.bib.
RegularizedProblems is a repository of optimization problems implemented in pure Julia.
Contrary to what the name suggests, the problems are not regularized but they should be.
However, the choice of regularizer is left to the user.
The problems concerned by the package have the form
minimize f(x) + h(x)
where f: ℝⁿ → ℝ has Lipschitz-continuous gradient and h: ℝⁿ → ℝ is lower semi-continuous and proper. The smooth term f describes the objective to minimize while the role of the regularizer h is to select a solution with desirable properties: minimum norm, sparsity below a certain level, maximum sparsity, etc.
This repository gives access to several f terms. Regularizers h should be taken from ProximalOperators.jl.
Until this package is registered, use
pkg> add https://github.com/optimizers/RegularizedProblems.jlPlease refer to the documentation.
- A. Y. Aravkin, R. Baraldi and D. Orban, A Proximal Quasi-Newton Trust-Region Method for Nonsmooth Regularized Optimization, SIAM Journal on Optimization, 32(2), pp.900–929, 2022. Technical report: https://arxiv.org/abs/2103.15993
@article{aravkin-baraldi-orban-2022,
author = {Aravkin, Aleksandr Y. and Baraldi, Robert and Orban, Dominique},
title = {A Proximal Quasi-{N}ewton Trust-Region Method for Nonsmooth Regularized Optimization},
journal = {SIAM Journal on Optimization},
volume = {32},
number = {2},
pages = {900--929},
year = {2022},
doi = {10.1137/21M1409536},
abstract = { We develop a trust-region method for minimizing the sum of a smooth term (f) and a nonsmooth term (h), both of which can be nonconvex. Each iteration of our method minimizes a possibly nonconvex model of (f + h) in a trust region. The model coincides with (f + h) in value and subdifferential at the center. We establish global convergence to a first-order stationary point when (f) satisfies a smoothness condition that holds, in particular, when it has a Lipschitz-continuous gradient, and (h) is proper and lower semicontinuous. The model of (h) is required to be proper, lower semi-continuous and prox-bounded. Under these weak assumptions, we establish a worst-case (O(1/\epsilon^2)) iteration complexity bound that matches the best known complexity bound of standard trust-region methods for smooth optimization. We detail a special instance, named TR-PG, in which we use a limited-memory quasi-Newton model of (f) and compute a step with the proximal gradient method, resulting in a practical proximal quasi-Newton method. We establish similar convergence properties and complexity bound for a quadratic regularization variant, named R2, and provide an interpretation as a proximal gradient method with adaptive step size for nonconvex problems. R2 may also be used to compute steps inside the trust-region method, resulting in an implementation named TR-R2. We describe our Julia implementations and report numerical results on inverse problems from sparse optimization and signal processing. Both TR-PG and TR-R2 exhibit promising performance and compare favorably with two linesearch proximal quasi-Newton methods based on convex models. }
}