Worst-case evaluation complexity for unconstrained nonlinear optimization using high-order regularized models

E. G. Birgin; J. L. Gardenghi; J. M. Martínez; S. A. Santos; P. L. Toint

doi:10.1007/s10107-016-1065-8

Worst-case evaluation complexity for unconstrained nonlinear optimization using high-order regularized models

E. G. Birgin, J. L. Gardenghi, J. M. Martínez, S. A. Santos, P. L. Toint

Namur Institute for Complex Systems

Research output: Contribution to journal › Article › peer-review

Abstract

The worst-case evaluation complexity for smooth (possibly nonconvex) unconstrained optimization is considered. It is shown that, if one is willing to use derivatives of the objective function up to order p (for p≥ 1 ) and to assume Lipschitz continuity of the p-th derivative, then an ϵ-approximate first-order critical point can be computed in at most O(ϵ ^- ⁽ ^p ⁺ ¹ ⁾ ^/ ^p) evaluations of the problem’s objective function and its derivatives. This generalizes and subsumes results known for p= 1 and p= 2.

Original language	English
Pages (from-to)	359-368
Number of pages	10
Journal	Mathematical Programming
Volume	163
Issue number	1-2
DOIs	https://doi.org/10.1007/s10107-016-1065-8
Publication status	Published - 15 Apr 2017

Keywords

Evaluation complexity
High-order models
Nonlinear optimization
Regularization
Unconstrained optimization

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10.1007/s10107-016-1065-8

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6 Invited talk

Worst-case evaluation complexity for nonconvex optimization: adventures in the jungle of high-order nonlinear optimization
Philippe Toint (Speaker)
8 Sep 2018
Activity: Talk or presentation types › Invited talk
A path and some adventures in the jungle of high-order nonlinear optimization
Philippe Toint (Speaker)
23 Oct 2017
Activity: Talk or presentation types › Invited talk
A path and some adventures in the jungle of high-order nonlinear optimization
Philippe Toint (Speaker)
24 Oct 2017
Activity: Talk or presentation types › Invited talk

Cite this

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title = "Worst-case evaluation complexity for unconstrained nonlinear optimization using high-order regularized models",

abstract = "The worst-case evaluation complexity for smooth (possibly nonconvex) unconstrained optimization is considered. It is shown that, if one is willing to use derivatives of the objective function up to order p (for p≥ 1 ) and to assume Lipschitz continuity of the p-th derivative, then an ϵ-approximate first-order critical point can be computed in at most O(ϵ - ( p + 1 ) / p) evaluations of the problem{\textquoteright}s objective function and its derivatives. This generalizes and subsumes results known for p= 1 and p= 2. ",

keywords = "Evaluation complexity, High-order models, Nonlinear optimization, Regularization, Unconstrained optimization",

author = "Birgin, {E. G.} and Gardenghi, {J. L.} and Mart{\'i}nez, {J. M.} and Santos, {S. A.} and Toint, {P. L.}",

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AU - Gardenghi, J. L.

AU - Martínez, J. M.

AU - Santos, S. A.

AU - Toint, P. L.

PY - 2017/4/15

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N2 - The worst-case evaluation complexity for smooth (possibly nonconvex) unconstrained optimization is considered. It is shown that, if one is willing to use derivatives of the objective function up to order p (for p≥ 1 ) and to assume Lipschitz continuity of the p-th derivative, then an ϵ-approximate first-order critical point can be computed in at most O(ϵ - ( p + 1 ) / p) evaluations of the problem’s objective function and its derivatives. This generalizes and subsumes results known for p= 1 and p= 2.

AB - The worst-case evaluation complexity for smooth (possibly nonconvex) unconstrained optimization is considered. It is shown that, if one is willing to use derivatives of the objective function up to order p (for p≥ 1 ) and to assume Lipschitz continuity of the p-th derivative, then an ϵ-approximate first-order critical point can be computed in at most O(ϵ - ( p + 1 ) / p) evaluations of the problem’s objective function and its derivatives. This generalizes and subsumes results known for p= 1 and p= 2.

KW - Evaluation complexity

KW - High-order models

KW - Nonlinear optimization

KW - Regularization

KW - Unconstrained optimization

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Worst-case evaluation complexity for unconstrained nonlinear optimization using high-order regularized models

Abstract

Keywords

Access to Document

Worst-case evaluation complexity for nonconvex optimization: adventures in the jungle of high-order nonlinear optimization

A path and some adventures in the jungle of high-order nonlinear optimization

A path and some adventures in the jungle of high-order nonlinear optimization

Cite this