Creation and analysis of biochemical constraint-based models using the COBRA Toolbox v. 3.0
Constraint-based reconstruction and analysis (COBRA) provides a molecular mechanistic
framework for integrative analysis of experimental molecular systems biology data and …
framework for integrative analysis of experimental molecular systems biology data and …
Open issues and recent advances in DC programming and DCA
HA Le Thi, T Pham Dinh - Journal of Global Optimization, 2024 - Springer
DC (difference of convex functions) programming and DC algorithm (DCA) are powerful
tools for nonsmooth nonconvex optimization. This field was created in 1985 by Pham Dinh …
tools for nonsmooth nonconvex optimization. This field was created in 1985 by Pham Dinh …
The boosted difference of convex functions algorithm for nonsmooth functions
FJ Aragón Artacho, PT Vuong - SIAM Journal on Optimization, 2020 - SIAM
The boosted difference of convex functions algorithm (BDCA) was recently proposed for
minimizing smooth difference of convex (DC) functions. BDCA accelerates the convergence …
minimizing smooth difference of convex (DC) functions. BDCA accelerates the convergence …
The ABC of DC programming
W de Oliveira - Set-Valued and Variational Analysis, 2020 - Springer
A function is called DC if it is expressible as the difference of two convex functions. In this
work, we present a short tutorial on difference-of-convex optimization surveying and …
work, we present a short tutorial on difference-of-convex optimization surveying and …
Convergence Analysis of the Proximal Gradient Method in the Presence of the Kurdyka–Łojasiewicz Property Without Global Lipschitz Assumptions
We consider a composite optimization problem where the sum of a continuously
differentiable and a merely lower semicontinuous function has to be minimized. The …
differentiable and a merely lower semicontinuous function has to be minimized. The …
On strongly quasiconvex functions: existence results and proximal point algorithms
F Lara - Journal of Optimization Theory and Applications, 2022 - Springer
We prove that every strongly quasiconvex function is 2-supercoercive (in particular,
coercive). Furthermore, we investigate the usual properties of proximal operators for strongly …
coercive). Furthermore, we investigate the usual properties of proximal operators for strongly …
Coderivative-based semi-Newton method in nonsmooth difference programming
FJ Aragón-Artacho, BS Mordukhovich… - Mathematical …, 2024 - Springer
This paper addresses the study of a new class of nonsmooth optimization problems, where
the objective is represented as a difference of two generally nonconvex functions. We …
the objective is represented as a difference of two generally nonconvex functions. We …
Large-scale optimization of partial auc in a range of false positive rates
The area under the ROC curve (AUC) is one of the most widely used performance measures
for classification models in machine learning. However, it summarizes the true positive rates …
for classification models in machine learning. However, it summarizes the true positive rates …
Multi-block Bregman proximal alternating linearized minimization and its application to orthogonal nonnegative matrix factorization
We introduce and analyze BPALM and A-BPALM, two multi-block proximal alternating
linearized minimization algorithms using Bregman distances for solving structured …
linearized minimization algorithms using Bregman distances for solving structured …
Local convergence of the Levenberg–Marquardt method under Hölder metric subregularity
We describe and analyse Levenberg–Marquardt methods for solving systems of nonlinear
equations. More specifically, we propose an adaptive formula for the Levenberg–Marquardt …
equations. More specifically, we propose an adaptive formula for the Levenberg–Marquardt …