93 citations · 232 across the 23 of their papers we have counts for
28 papers
A Barrier Primal Dual Hybrid Gradient Method for Solving Linear Programming Problems
Yingxin Zhou, Stefano Cipolla, Phan T. Vuong
Primal Dual Hybrid Gradient (PDHG) method has been verified to exhibit a two stage convergence behavior, in which a prolonged active set identification phase may be a major issue o…
Faster than Fast-LTS: Robust Regression and Outlier Detection with DC Programming
Marah-Lisanne Thormann, Phan Tu Vuong, Alain B. Zemkoho +1
When datasets contain outliers, robust regression is a well-established alternative to Ordinary Least Squares. A commonly employed robust estimator is Least Trimmed Squares (LTS),…
The Popov's Algorithm with Optimal Bounded Stepsize for Generalized Monotone Variational Inequalities
Nhung Hong Nguyen, Thanh Quoc Trinh, Phan Tu Vuong
For solving unconstrained generalized monotone variational inequality, we prove that the iterations generated by Popov algorithm converge to a solution when stepsize is smaller tha…
Second order continuous and discrete dynamical systems for solving inverse quasi-variational inequalities
Pham Viet Hai, Thanh Quoc Trinh, Phan Tu Vuong
In this paper, we investigate the inverse quasi-variational inequality problem in finite-dimensional spaces. First, we introduce a second-order dynamical system whose trajectory co…
Adversarial training with restricted data manipulation
David Benfield, Stefano Coniglio, Phan Tu Vuong +1
Adversarial machine learning concerns situations in which learners face attacks from active adversaries. Such scenarios arise in applications such as spam email filtering, malware…
Countering adversarial evasion in regression analysis
David Benfield, Phan Tu Vuong, Alain Zemkoho
Adversarial machine learning challenges the assumption that the underlying distribution remains consistent throughout the training and implementation of a prediction model. In part…
Anderson Accelerated Primal-Dual Hybrid Gradient for solving LP
Yingxin Zhou, Stefano Cipolla, Phan Tu Vuong
We present the Anderson Accelerated Primal--Dual Hybrid Gradient (AA-PDHG), a fixed-point-based framework that integrates Anderson Acceleration into the PDHG method for solving lin…
Heavy Ball and Nesterov Accelerations with Hessian-driven Damping for Nonconvex Optimization
N. Hadjisavvas, F. Lara, R. T. Marcavillaca +1
In this work, we investigate a second-order dynamical system with Hessian-driven damping tailored for a class of nonconvex functions called strongly quasiconvex. Buil\-ding upon th…
Classification under strategic adversary manipulation using pessimistic bilevel optimisation
David Benfield, Stefano Coniglio, Martin Kunc +2
Adversarial machine learning concerns situations in which learners face attacks from active adversaries. Such scenarios arise in applications such as spam email filtering, malware…
Characterizations, Dynamical Systems and Gradient Methods for Strongly Quasiconvex Functions
Felipe Lara, Raúl T. Marcavillaca, Phan T. Vuong
We study differentiable strongly quasiconvex functions for providing new properties for algorithmic and monotonicity purposes. Furthemore, we provide insights into the decreasing b…
A fixed-time stable forward-backward dynamical system for solving generalized monotone inclusions
Nam V Tran, Hai T. T. Le, An V. Truong +1
We propose a forward-backward splitting dynamical system for solving inclusion problems of the form in Hilbert spaces, where is a maximal operator and is a…
A third order dynamical system for generalized monotone equation
Pham Viet Hai, Phan Tu Vuong
We propose a third order dynamical system for solving a nonlinear equation in Hilbert spaces where the operator is cocoercive with respect to the solutions set. Under mild conditio…
The Boosted Difference of Convex Functions Algorithm for Value-at-Risk Constrained Portfolio Optimization
Marah-Lisanne Thormann, Phan Tu Vuong, Alain B. Zemkoho
A highly relevant problem of modern finance is the design of Value-at-Risk (VaR) optimal portfolios. Due to contemporary financial regulations, banks and other financial institutio…
The Boosted DC Algorithm for Clustering with Constraints
Tuyen Tran, Kate Figenschou, Phan Tu Vuong
This paper aims to investigate the effectiveness of the recently proposed Boosted Difference of Convex functions Algorithm (BDCA) when applied to clustering with constraints and se…
Stochastic Relaxed Inertial Forward-Backward-Forward splitting for Monotone Inclusions in Hilbert spaces
Shisheng Cui, Uday V. Shanbhag, Mathias Staudigl +1
We consider monotone inclusions defined on a Hilbert space where the operator is given by the sum of a maximal monotone operator and a single-valued monotone, Lipschitz continu…
Computing Dynamic User Equilibrium on Large-Scale Networks Without Knowing Global Parameters
Duong Viet Thong, Aviv Gibali, Mathias Staudigl +1
Dynamic user equilibrium (DUE) is a Nash-like solution concept describing an equilibrium in dynamic traffic systems over a fixed planning period. DUE is a challenging class of equi…
A Relaxed Inertial Forward-Backward-Forward Algorithm for Solving Monotone Inclusions with Application to GANs
Radu Ioan Bot, Michael Sedlmayer, Phan Tu Vuong
We introduce a relaxed inertial forward-backward-forward (RIFBF) splitting algorithm for approaching the set of zeros of the sum of a maximally monotone operator and a single-value…
Strong Convergence of Forward-Backward-Forward Methods for Pseudo-monotone Variational Inequalities with Applications to Dynamic User Equilibrium in Traffic Networks
Benoit Duvocelle, Dennis Meier, Mathias Staudigl +1
In infinite-dimensional Hilbert spaces we device a class of strongly convergent primal-dual schemes for solving variational inequalities defined by a Lipschitz continuous and pseud…
The Boosted DC Algorithm for linearly constrained DC programming
Francisco J. Aragón Artacho, Rubén Campoy, Phan T. Vuong
The Boosted Difference of Convex functions Algorithm (BDCA) has been recently introduced to accelerate the performance of the classical Difference of Convex functions Algorithm (DC…
Using positive spanning sets to achieve d-stationarity with the Boosted DC Algorithm
Francisco J. Aragón Artacho, Rubén Campoy, Phan T. Vuong
The Difference of Convex functions Algorithm (DCA) is widely used for minimizing the difference of two convex functions. A recently proposed accelerated version, termed BDCA for Bo…
Forward-backward-forward methods with variance reduction for stochastic variational inequalities
Radu Ioan Bot, Panayotis Mertikopoulos, Mathias Staudigl +1
We develop a new stochastic algorithm with variance reduction for solving pseudo-monotone stochastic variational inequalities. Our method builds on Tseng's forward-backward-forward…
Finding Zeros of Hölder Metrically Subregular Mappings via Globally Convergent Levenberg-Marquardt Methods
Masoud Ahookhosh, Ronan M. T. Fleming, Phan T. Vuong
We present two globally convergent Levenberg-Marquardt methods for finding zeros of Hölder metrically subregular mappings that may have non-isolated zeros. The first method unifies…
The Boosted DC Algorithm for nonsmooth functions
Francisco J. Aragón Artacho, Phan T. Vuong
The Boosted Difference of Convex functions Algorithm (BDCA) was recently proposed for minimizing smooth difference of convex (DC) functions. BDCA accelerates the convergence of the…
An inertial extrapolation method for convex simple bilevel optimization
Yekini Shehu, Phan Tu Vuong, Alain Zemkoho
We consider a scalar objective minimization problem over the solution set of another optimization problem. This problem is known as simple bilevel optimization problem and has draw…