93 citations · 217 across the 21 of their papers we have counts for
22 papers
First-order majorization-minimization meets high-order majorant: Boosted inexact high-order forward-backward method
Alireza Kabgani, Masoud Ahookhosh
This paper introduces a first-order majorization-minimization framework based on a high-order majorant for continuous functions, incorporating a non-quadratic regularization term o…
Asymptotic Convergence Analysis of High-Order Proximal-Point Methods Beyond Sublinear Rates
Masoud Ahookhosh, Alfredo Iusem, Alireza Kabgani +1
This paper investigates the asymptotic convergence behavior of the high-order proximal-point algorithm (HiPPA) to global minimizers, extending existing analyses beyond sublinear co…
Moreau envelope and proximal-point methods under the lens of high-order regularization
Alireza Kabgani, Masoud Ahookhosh
This paper is devoted to investigating the fundamental properties of the high-order proximal operator (HOPE) and the high-order Moreau envelope (HOME) in the nonconvex setting, whe…
ItsDEAL: Inexact two-level smoothing descent algorithms for weakly convex optimization
Alireza Kabgani, Masoud Ahookhosh
This paper addresses nonconvex optimization problems via a two-level smoothing framework, where the high-order Moreau envelope (HOME) provides a smooth approximation of weakly conv…
ItsOPT: An inexact two-level smoothing framework for nonconvex optimization via high-order Moreau envelope
Alireza Kabgani, Masoud Ahookhosh
This paper introduces ItsOPT, an {\it inexact two-level smoothing optimization framework} designed to find first-order critical points of nonsmooth and nonconvex functions. The fra…
High-order methods beyond the classical complexity bounds, II: inexact high-order proximal-point methods with segment search
Masoud Ahookhosh, Yurii Nesterov
A bi-level optimization framework (BiOPT) was proposed in [3] for convex composite optimization, which is a generalization of bi-level unconstrained minimization framework (BLUM) g…
High-order methods beyond the classical complexity bounds, I: inexact high-order proximal-point methods
Masoud Ahookhosh, Yurii Nesterov
In this paper, we introduce a \textit{Bi-level OPTimization} (BiOPT) framework for minimizing the sum of two convex functions, where both can be nonsmooth. The BiOPT framework invo…
Block Alternating Bregman Majorization Minimization with Extrapolation
Le Thi Khanh Hien, Duy Nhat Phan, Nicolas Gillis +2
In this paper, we consider a class of nonsmooth nonconvex optimization problems whose objective is the sum of a block relative smooth function and a proper and lower semicontinuous…
Bregman Finito/MISO for nonconvex regularized finite sum minimization without Lipschitz gradient continuity
Puya Latafat, Andreas Themelis, Masoud Ahookhosh +1
We introduce two algorithms for nonconvex regularized finite sum minimization, where typical Lipschitz differentiability assumptions are relaxed to the notion of relative smoothnes…
A block inertial Bregman proximal algorithm for nonsmooth nonconvex problems with application to symmetric nonnegative matrix tri-factorization
Masoud Ahookhosh, Le Thi Khanh Hien, Nicolas Gillis +1
We propose BIBPA, a block inertial Bregman proximal algorithm for minimizing the sum of a block relatively smooth function (that is, relatively smooth concerning each block) and bl…
Multi-block Bregman proximal alternating linearized minimization and its application to orthogonal nonnegative matrix factorization
Masoud Ahookhosh, Le Thi Khanh Hien, Nicolas Gillis +1
We introduce and analyze BPALM and A-BPALM, two multi-block proximal alternating linearized minimization algorithms using Bregman distances for solving structured nonconvex problem…
A Bregman forward-backward linesearch algorithm for nonconvex composite optimization: superlinear convergence to nonisolated local minima
Masoud Ahookhosh, Andreas Themelis, Panagiotis Patrinos
We introduce Bella, a locally superlinearly convergent Bregman forward backward splitting method for minimizing the sum of two nonconvex functions, one of which satisfying a relati…
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…
On the acceleration of forward-backward splitting via an inexact Newton method
Andreas Themelis, Masoud Ahookhosh, Panagiotis Patrinos
We propose a Forward-Backward Truncated-Newton method (FBTN) for minimizing the sum of two convex functions, one of which smooth. Unlike other proximal Newton methods, our approach…
Creation and analysis of biochemical constraint-based models: the COBRA Toolbox v3.0
Laurent Heirendt, Sylvain Arreckx, Thomas Pfau +52
COnstraint-Based Reconstruction and Analysis (COBRA) provides a molecular mechanistic framework for integrative analysis of experimental data and quantitative prediction of physico…
Local convergence of the Levenberg-Marquardt method under Hölder metric subregularity
Masoud Ahookhosh, Francisco J. Aragón Artacho, Ronan M. T. Fleming +1
We describe and analyse Levenberg-Marquardt methods for solving systems of nonlinear equations. More specifically, we propose an adaptive formula for the Levenberg-Marquardt parame…
Accelerated first-order methods for large-scale convex minimization
Masoud Ahookhosh
This paper discusses several (sub)gradient methods attaining the optimal complexity for smooth problems with Lipschitz continuous gradients, nonsmooth problems with bounded variati…
Two globally convergent nonmonotone trust-region methods for unconstrained optimization
Masoud Ahookhosh, Susan Ghaderi
This paper addresses some trust-region methods equipped with nonmonotone strategies for solving nonlinear unconstrained optimization problems. More specifically, the importance of…
An optimal subgradient algorithm for large-scale bound-constrained convex optimization
Masoud Ahookhosh, Arnold Neumaier
This paper shows that the OSGA algorithm -- which uses first-order information to solve convex optimization problems with optimal complexity -- can be used to efficiently solve arb…
An optimal subgradient algorithm for large-scale convex optimization in simple domains
Masoud Ahookhosh, Arnold Neumaier
This paper shows that the optimal subgradient algorithm, OSGA, proposed in \cite{NeuO} can be used for solving structured large-scale convex constrained optimization problems. Only…
On efficiency of nonmonotone Armijo-type line searches
Masoud Ahookhosh, Susan Ghaderi
Monotonicity and nonmonotonicity play a key role in studying the global convergence and the efficiency of iterative schemes employed in the field of nonlinear optimization, where g…
Optimal subgradient algorithms with application to large-scale linear inverse problems
Masoud Ahookhosh
This study addresses some algorithms for solving structured unconstrained convex optimiza- tion problems using first-order information where the underlying function includes high-d…