Dušan Jakovetić,Nataša Krejić,Greta Malaspina
Dušan Jakovetić
We consider two formulations for distributed optimization wherein N nodes in a generic connected network solve a problem of common interest: distributed personalized optimization and consensus optimization. A new method termed DINAS (Distri...
Marta Lazzaretti,Claudio Estatico,Alejandro Melero et al.
Marta Lazzaretti et al.
Off-the-grid regularisation has been extensively employed over the last decade in the context of ill-posed inverse problems formulated in the continuous setting of the space of Radon measures M ( Ω ) . These approaches enjoy convexity...
A fast continuous time approach for non-smooth convex optimization using Tikhonov regularization technique [0.03%]
使用Tikhonov正则化技术进行非光滑凸优化的快速连续时间方法
Mikhail A Karapetyants
Mikhail A Karapetyants
In this paper we would like to address the classical optimization problem of minimizing a proper, convex and lower semicontinuous function via the second order in time dynamics, combining viscous and Hessian-driven damping with a Tikhonov r...
On the SCD semismooth* Newton method for generalized equations with application to a class of static contact problems with Coulomb friction [0.03%]
具有一种静摩擦接触问题类的广义方程的SCD半光滑*牛顿法研究
Helmut Gfrerer,Michael Mandlmayr,Jiří V Outrata et al.
Helmut Gfrerer et al.
In the paper, a variant of the semismooth∗ Newton method is developed for the numerical solution of generalized equations, in which the multi-valued part is a so-called SCD (subspace containing derivative) mapping. Under a rather mild...
Alberto Domínguez Corella,Nicolai Jork,Vladimir M Veliov
Alberto Domínguez Corella
The paper investigates stability properties of solutions of optimal control problems constrained by semilinear parabolic partial differential equations. Hölder or Lipschitz dependence of the optimal solution on perturbations are obtained f...
An accelerated minimax algorithm for convex-concave saddle point problems with nonsmooth coupling function [0.03%]
一种求解非光滑耦合函数的凸凹鞍点问题的加速最小-最大算法
Radu Ioan Boţ,Ernö Robert Csetnek,Michael Sedlmayer
Radu Ioan Boţ
In this work we aim to solve a convex-concave saddle point problem, where the convex-concave coupling function is smooth in one variable and nonsmooth in the other and not assumed to be linear in either. The problem is augmented by a nonsmo...
Immanuel M Bomze,Bo Peng
Immanuel M Bomze
We study (nonconvex) quadratic optimization problems with complementarity constraints, establishing an exact completely positive reformulation under-apparently new-mild conditions involving only the constraints, not the objective. Moreover,...
Constrained and unconstrained deep image prior optimization models with automatic regularization [0.03%]
具有自动正则化的约束和非约束深度图像先验优化模型
Pasquale Cascarano,Giorgia Franchini,Erich Kobler et al.
Pasquale Cascarano et al.
Deep Image Prior (DIP) is currently among the most efficient unsupervised deep learning based methods for ill-posed inverse problems in imaging. This novel framework relies on the implicit regularization provided by representing images as t...
Amadeu A Coco,Andréa Cynthia Santos,Thiago F Noronha
Amadeu A Coco
This article deals with two min-max regret covering problems: the min-max regret Weighted Set Covering Problem (min-max regret WSCP) and the min-max regret Maximum Benefit Set Covering Problem (min-max regret MSCP). These problems are the r...
Iterative regularization for constrained minimization formulations of nonlinear inverse problems [0.03%]
非线性反问题约束最小化形式的迭代正则化方法
Barbara Kaltenbacher,Kha Van Huynh
Barbara Kaltenbacher
In this paper we study the formulation of inverse problems as constrained minimization problems and their iterative solution by gradient or Newton type methods. We carry out a convergence analysis in the sense of regularization methods and ...