most citedItsOPT: An inexact two-level smoothing framework for nonconvex optimization via high-order Moreau envelope

1 citations · 1 across the 2 of their papers we have counts for

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12 papers

math.OC20261 cited

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…

math.OC2026

Difference-of-Convex Optimization via Inexact Smoothing Descent Methods: Difference of High-Order Moreau Envelopes

Alireza Kabgani, Moslem Zamani, Masoud Ahookhosh

This paper studies difference-of-convex (DC) optimization problems through smoothing descent techniques. In particular, we introduce the difference of high-order Moreau envelopes (…

math.OC2026

On fundamental properties of high-order forward-backward envelope

Alireza Kabgani, Masoud Ahookhosh

This paper studies the fundamental properties of the high-order forward-backward splitting mapping (HiFBS) and its associated high-order forward-backward envelope (HiFBE) through t…

math.OC2026

Minimizing Smooth Kurdyka-Łojasiewicz Functions via Generalized Descent Methods: Convergence Rate and Complexity

Masoud Ahookhosh, Susan Ghaderi, Alireza Kabgani +1

This paper introduces a generalized descent algorithm (DEAL) for minimizing smooth nonconvex functions. If the objective function is nonsmooth, a smoothing technique (e.g., forward…

math.ST2026

Weak-Curvature AMISE and Plug-in Bandwidth Selection for Kernel Density Estimation

Alireza Kabgani, Elaheh Lotfian

Kernel density estimation risk expansions are commonly expressed through the integrated squared curvature term that enters second-order AMISE and plug-in bandwidth rules. This pape…

math.OC2026

Speeding Up Nonsmooth Bayesian MCMC Sampling via Inexact Proximal Unadjusted Langevin Algorithm

Susan Ghaderi, Alireza Kabgani, Yves Moreau +1

We study sampling from posterior distributions with nonsmooth composite potentials, a setting in which proximal-based Langevin methods are theoretically appealing but in practice l…