If you use ShiftedProximalOperators.jl in your work, please cite using the format given in CITATION.bib.
ShiftedProximalOperators is a library of proximal operators associated with proper lower-semicontinuous functions such as those implemented in ProximalOperators.jl for use in the algorithms implemented in RegularizedOptimization.jl.
The main difference between the proximal operators implemented in ProximalOperators.jl is that those implemented here involve a translation of the nonsmooth term. Specifically, this package considers proximal operators defined as
argmin { ½ ‖t - q‖₂² + ν h(x + s + t) + χ(s + t; ΔB) | t ∈ ℝⁿ },
where q is given, x and s are fixed shifts, h is the nonsmooth term with respect to which we are computing the proximal operator, and χ(.; ΔB) is the indicator of a ball of radius Δ defined by a certain norm.
Until this package is registered, use
pkg> add https://github.com/rjbaraldi/ShiftedProximalOperators.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. }
}