Convergence Rate of Proximal Inertial Algorithms Associated with Moreau Envelopes of Convex Functions

  • Hedy AttouchEmail author
  • Juan Peypouquet


In a Hilbert space setting \({\mathcal H}\), we develop new inertial proximal-based algorithms that aim to rapidly minimize a convex lower-semicontinuous proper function \(\varPhi : \mathcal H \rightarrow {\mathbb R} \cup \{+\infty \}\). The guiding idea is to use an accelerated proximal scheme where, at each step, Φ is replaced by its Moreau envelope, with varying approximation parameter. This leads to consider a Relaxed Inertial Proximal Algorithm (RIPA) with variable parameters which take into account the effects of inertia, relaxation, and approximation. (RIPA) was first introduced to solve general maximally monotone inclusions, in which case a judicious adjustment of the parameters makes it possible to obtain the convergence of the iterates towards the equilibria. In the case of convex minimization problems, convergence analysis of (RIPA) was initially addressed by Attouch and Cabot, based on its formulation as an inertial gradient method with varying potential functions. We propose a new approach to this algorithm, along with further developments, based on its formulation as a proximal algorithm associated with varying Moreau envelopes. For convenient choices of the parameters, we show the fast optimization property of the function values, with the order o(k−2), and the weak convergence of the iterates. This is in line with the recent studies of Su-Boyd-Candès, Chambolle-Dossal, Attouch-Peypouquet. We study the impact of geometric assumptions on the convergence rates, and the stability of the results with respect to perturbations and errors. Finally, in the case of structured minimization problems smooth + nonsmooth, based on this approach, we introduce new proximal-gradient inertial algorithms for which similar convergence rates are shown.


Inertial proximal algorithms Lyapunov analysis Maximally monotone operators Moreau envelopes Nesterov accelerated gradient method Nonsmooth convex minimization Proximal-gradient algorithms Relaxation 

AMS Subject Classification

37N40 46N10 49M30 65K05 65K10 90B50 90C25 



This work was supported by FONDECYT Grant 1181179 and CMM-Conicyt PIA AFB170001.


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Authors and Affiliations

  1. 1.IMAGUniv. Montpellier, CNRSMontpellierFrance
  2. 2.Departamento de Ingeniería Matemática & Centro de Modelamiento Matemático (CNRS UMI2807), FCFMUniversidad de ChileSantiagoChile

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