Abstract
In a Hilbert framework, we discuss a continuous Newton-like model that is well-adapted in view to numerical purposes for solving convex minimization and more general monotone inclusion problems. Algorithmic solutions to these problems were recently inspired by implicit temporal discretizations of the (stabilized) continuous version of Nesterov’s accelerated gradient method with an additional Hessian damping term (so as to attenuate the oscillation effects). Unfortunately, due to the presence of the Hessian term, these discrete variants require several gradients or proximal evaluations (per iteration). An alternative methodology can be realized by means of a first-order model that no more involves the Hessian term and that can be extended to the case of an arbitrary maximally monotone operator. Our first-order model originates from the reformulation of a closely related variant to the Nesterov-like equation. Its dynamics are studied (simultaneously) with regard to convex minimization and monotone inclusion problems by considering it when governed by the sum of the gradient of a convex differentiable function and (up to a multiplicative constant) the Yosida approximation of a maximally monotone operator, with an appropriate adjustment of the regularization parameter. It turns out that our model, offers a new framework for discrete variants, while keeping the main asymptotic features of the (stabilized) Nesterov-like equation. Two new algorithms are then suggested relative to the considered optimization problems.
Similar content being viewed by others
References
Alvarez, F., Attouch, H., Bolte, J., Redont, P.: A second-order gradient-like dissipative dynamical system with Hessian driven damping. Application to Optimization and Mechanics. J. Math. Pures Appl. 81(8), 747–779 (2002)
Attouch, H., Balhag, A., Chbani, Z., Riahi, H.: Fast convex optimization via inertial dynamics combining viscous and Hessian-driven damping with time rescaling. Evolution Equations and Control Theory. https://doi.org/10.3934/eect.2021010. arXiv:2009.07620v1 (2020) (2021)
Attouch, H., Cabot, A.: Convergence rates of inertial forward-backward algorithms. SIAM J. Optim. 28(1), 849–874 (2018)
Attouch, H., Chbani, Z., Fadili, J., Riahi, H.: First-order optimization algorithms via inertial systems with Hessian driven damping. Math. Programming. https://doi.org/10.1007/s10107-020-01591-1. arXiv:1907.10536 (2019)
Attouch, H., Laszlo, S.C.: Newton-like inertial dynamics and proximal algorithms governed by maximally monotone operators. SIAM Journal on Optimization 30(4). https://hal.archives-ouvertes.fr/hal-02549730 (2020)
Attouch, H., Laszlo, S.C.: Continuous Newton-like inertial dynamics for monotone inclusions set-valued and variational Analysis. https://doi.org/10.1007/s11228-020-00564-y (2020)
Attouch, H., Peypouquet, J.: The rate of convergence of Nesterov’s accelerated forward-backward method is actually faster than 1/k2. SIAM J. Optimization 26(3), 1824–1834 (2016)
Attouch, H., Peypouquet, J.: Convergence of inertial dynamics and proximal algorithms governed by maximally monotone operators. Math. Programming 174, 391–432 (2019)
Attouch, H., Peypouquet, J., Redont, P.: Fast convex minimization via inertial dynamics with Hessian driven damping. J. Differential Equations 261, 5734–5783 (2016)
Belgioioso, G., Grammatico, S.: Semi-Decentralized Nash equilibrium seeking in aggregative games with separable coupling constraints and Non-Differentiable cost functions. IEEE Control Systems Letters 1(2), 400–405 (2017)
Boţ, R.I., Csetnek, E.R.: Second order forward-backward dynamical systems for monotone inclusion problems. SIAM J. Control. Optim. 54(3), 1423–1443 (2016)
Brezis, H.: Opérateurs Maximaux Monotones Et Semi-Groupes De Contractions Dans Les Espaces De Hilbert. Math. Stud., vol. 5. North-Holland, Amsterdam (1973)
Brezis, H.: Function Analysis, Sobolev Spaces and Partial Differential Equations. Springer, New York (2010). https://doi.org/10.1007/978-0-387-70914-7
Briceño-Arias, L.M., Combettes, P.L., et al.: Monotone Operator Methods for Nash Equilibria in Non-potential Games. In: Bailey, D. (ed.) Computational and Analytical Mathematics. Springer Proceedings in Mathematics & Statistics, p 50. Springer, New York (2013)
Csetnek, E.R.: Continuous dynamics related to monotone inclusions and non-smooth optimization problems. Set Valued and Variational Analysis 28, 611–642 (2020)
Deimling, K.: Zeros of accretive operators. Manuscripta Mathematica 13(4), 365–374 (1974)
Haraux, A.: Systèmes dynamiques dissipatifs et applications. RMA17 Masson (1991)
Malitsky, Y.: Proximal extrapolated gradient methods for variational inequalities. Optimization Methods and Software 33(1), 140–164 (2018)
Nesterov, Y.: Introductory Lectures on Convex Optimization: A Basic Course, Volume 87 of Applied Optimization. Kluwer Academic Publishers, Boston (2004)
Nesterov, Y.: Smooth minimization of non-smooth functions. Mathematical Programming 103 (1), 127–152 (2005)
Nesterov, Y.: Gradient methods for minimizing composite objective function. Math. Programming 140, 125–161 (2013)
Ochs, P., Brox, T., Pock. T.: ipiasco: inertial proximal algorithm for strongly convex optimization. J Math Imaging Vision 53, 171–181 (2015)
Opial, Z.: Weak convergence of the sequence of successive approximations for nonexpansive mappings. Bull. Amer. Math. Soc. 73, 591–597 (1967)
Reich, S.: An iterative procedure for constructing zeros of accretive sets in Banach spaces. Nonlinear Analysis: TMA 2(1), 85–92 (1978)
Rockafellar, R.T., Wets, R.J.-B.: Variational Analysis Fundamental Principles of Mathematical Sciences. Springer 317, Berlin (1998)
Shi, B., Du, S.S., Jordan, M.I., Su, W.J.: Understanding the Acceleration Phenomenon via High-Resolution Differential Equations. arXiv:1810.08907v3 (2018)
Su, W., Boyd, S., Candès, E.J.: A differential equation for modeling Nesterov’s accelerated gradient method: theory and insights. Neural Information Processing Systems 27, 2510–2518 (2014)
Acknowledgements
The authors would like to thank the two anonymous referees for their careful readings of the manuscript and their insightful comments and observations.
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Appendix
Appendix
1.1 A.1 The Yosida Approximation
The Yosida approximation enjoys numerous nice properties which often facilitating and simplifying calculations. Some of them are recalled below (see [12, 13]):
Proposition A.1
Let \(A : {\mathscr{H}} \to 2^{{\mathscr{H}}}\) be a maximal monotone operator and set JλA := (I + λA)− 1. Then, we have the following properties :
-
(a)
JλA is single-valued, everywhere defined and nonexpansive,
-
(b)
\( \forall v\in {\mathscr{H}}, \forall \lambda >0, A_{\lambda } v \in A(J_{\lambda A}v),\)
-
(c)
\(\forall (u,v)\in {\mathscr{H}}^{2}, \forall \lambda >0, ~\langle A_{\lambda } u - A_{\lambda } v, u - v \rangle \geq \lambda \|A_{\lambda } u - A_{\lambda } v\|^{2},\)
-
(d)
\(\forall (u,v)\in {\mathscr{H}}^{2}, \forall \lambda >0, ~\| A_{\lambda } u - A_{\lambda } v\| \leq \frac {1}{\lambda }\|u - v\|,\)
-
(e)
\(\forall \lambda >0, A^{-1}(\{0\}) = A_{\lambda }^{-1}(\{0\}),\)
-
(f)
∀λ > 0,∀μ > 0, (Aλ)μ = Aλ+μ.
Lemma A.1
Let γ,δ > 0 and \(x, y \in {\mathscr{H}}\). Then for z ∈ A− 1({0}), we have
Proof
The proof of (A.1) can be found in [8]. Let us prove (A.2). To get this we simply have
\(\begin {array}{l} A_{\gamma }x- A_{\delta }y= \frac {1}{ \delta } \left (\delta A_{\gamma }x- \delta A_{\delta }y\right ) \\ \hskip 2cm = \frac {1}{ \delta } \left ((\delta -\gamma ) A_{\gamma }x + (\gamma A_{\gamma }x -\delta A_{\delta }y\right ), \end {array}\)
hence
\(\begin {array}{l} \| A_{\gamma }x- A_{\delta }y \| \le \frac {1}{ \delta } \left (| \delta -\gamma | \times \|A_{\gamma }x \|+ \| \gamma A_{\gamma }x -\delta A_{\delta }y\| \right ). \end {array}\)
Consequently, by \( \|A_{\gamma }x\|\le \frac {1}{\gamma } \|x-z\|\) and using (A.1), we obtain
\(\begin {array}{l} \| A_{\gamma }x- A_{\delta }y \| \le \frac {1}{ \delta } \left (\frac {| \delta -\gamma |}{\gamma } \times \|x-z\| + 2\|x-y\| + 2\frac {|\gamma - \delta |}{\gamma }\|x-z\| \right ) \\ \hskip 2.cm = \frac {1}{ \delta } \left (3\frac {| \delta -\gamma |}{\gamma } \times \|x-z\| + 2\|x-y\| \right ), \end {array}\) that is the desired inequality □
1.2 A.2 A Technical Result
Lemma A.2 (8 Lemma A.5)
Let \(\omega ,\eta : [0,+\infty [ \to [0,+\infty [\) be absolutely continuous functions such that \(\eta \notin L^{1}(0,+\infty )\) and which satisfy \({\int \limits }_{0}^{+\infty } \omega (t)\eta (t) dt < \infty \), along with \(|\dot {\omega }(t)|\leq \eta (t)\) for almost every t > 0. Then \(\displaystyle \lim _{t\to +\infty } \omega (t) = 0\).
Rights and permissions
About this article
Cite this article
Labarre, F., Maingé, PE. First-Order Frameworks for Continuous Newton-like Dynamics Governed by Maximally Monotone Operators. Set-Valued Var. Anal 30, 425–451 (2022). https://doi.org/10.1007/s11228-021-00593-1
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11228-021-00593-1
Keywords
- Asymptotic behavior
- First-order differential equation
- Dissipative systems
- Maximal monotone operators
- Yosida approximation
- Coupled systems
- Nesterov acceleration
- Newton-like convergence