A derivative-free trust-funnel method for equality-constrained nonlinear optimization

Phillipe Rodrigues Sampaio; Ph L. Toint

doi:10.1007/s10589-014-9715-3

A derivative-free trust-funnel method for equality-constrained nonlinear optimization

Phillipe Rodrigues Sampaio, Ph L. Toint

Namur Institute for Complex Systems

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

Abstract

Abstract: A new derivative-free method is proposed for solving equality-constrained nonlinear optimization problems. This method is of the trust-funnel variety and is also based on the use of polynomial interpolation models. In addition, it uses a self-correcting geometry procedure in order to ensure that the interpolation problem is well defined in the sense that the geometry of the set of interpolation points does not differ too much from the ideal one. The algorithm is described in detail and some encouraging numerical results are presented.

Graphical Abstract: [Figure not available: see fulltext.]

Original language	English
Pages (from-to)	25-49
Number of pages	25
Journal	Computational Optimization and Applications
Volume	61
Issue number	1
DOIs	https://doi.org/10.1007/s10589-014-9715-3
Publication status	Published - 1 May 2015

Keywords

Derivative-free optimization
Equality constraints
Trust funnel
Trust-region method

Access to Document

10.1007/s10589-014-9715-3

http://link.springer.com/article/10.1007/s10589-014-9715-3

14 Citations
6 Article

An Interior-Point Trust-Funnel Algorithm for Nonlinear Optimization
Curtis, F., Gould, N. I. M., Robinson, D. & Toint, P., Jan 2017, In: Mathematical Programming. 161, 1-2, p. 73-134 62 p.
Research output: Contribution to journal › Article › peer-review

File
13 Downloads (Pure)
BFO, a trainable derivative-free Brute Force Optimizer for nonlinear bound-constrained optimization and equilibrium computations with continuous and discrete variables
Porcelli, M. & Toint, P., 30 Jun 2017, In: Transactions of the American Methematical Society on Mathematical Software. 44, 1, 28 p., 6.
Research output: Contribution to journal › Article › peer-review

File
19 Downloads (Pure)
Quasi-Newton updates with weighted secant equations
Gratton, S., Malmedy, V. & Toint, P., 4 Jul 2015, In: Optimization Methods and Software. 30, 4, p. 748-755 8 p.
Research output: Contribution to journal › Article › peer-review

File
122 Downloads (Pure)

2 Active

DFO: Derivative free numerical algorithms for optimization
TOINT, P., COLSON, B., Gratton, S., Tröltzsch, A. & RODRIGUES SAMPAIO, P.
1/03/94 → …
Project: Research
ADALGOPT: ADALGOPT - Advanced algorithms in nonlinear optimization
Sartenaer, A. & TOINT, P.
1/01/87 → …
Project: Research Axis

Cite this

@article{e4a5aaed974c4111a7a5501411aee457,

title = "A derivative-free trust-funnel method for equality-constrained nonlinear optimization",

abstract = "Abstract: A new derivative-free method is proposed for solving equality-constrained nonlinear optimization problems. This method is of the trust-funnel variety and is also based on the use of polynomial interpolation models. In addition, it uses a self-correcting geometry procedure in order to ensure that the interpolation problem is well defined in the sense that the geometry of the set of interpolation points does not differ too much from the ideal one. The algorithm is described in detail and some encouraging numerical results are presented.Graphical Abstract: [Figure not available: see fulltext.]",

keywords = "Derivative-free optimization, Equality constraints, Trust funnel, Trust-region method",

author = "{Rodrigues Sampaio}, Phillipe and Toint, {Ph L.}",

year = "2015",

month = may,

day = "1",

doi = "10.1007/s10589-014-9715-3",

language = "English",

volume = "61",

pages = "25--49",

journal = "Computational Optimization and Applications",

issn = "0926-6003",

publisher = "Springer Netherlands",

number = "1",

}

TY - JOUR

T1 - A derivative-free trust-funnel method for equality-constrained nonlinear optimization

AU - Rodrigues Sampaio, Phillipe

AU - Toint, Ph L.

PY - 2015/5/1

Y1 - 2015/5/1

N2 - Abstract: A new derivative-free method is proposed for solving equality-constrained nonlinear optimization problems. This method is of the trust-funnel variety and is also based on the use of polynomial interpolation models. In addition, it uses a self-correcting geometry procedure in order to ensure that the interpolation problem is well defined in the sense that the geometry of the set of interpolation points does not differ too much from the ideal one. The algorithm is described in detail and some encouraging numerical results are presented.Graphical Abstract: [Figure not available: see fulltext.]

AB - Abstract: A new derivative-free method is proposed for solving equality-constrained nonlinear optimization problems. This method is of the trust-funnel variety and is also based on the use of polynomial interpolation models. In addition, it uses a self-correcting geometry procedure in order to ensure that the interpolation problem is well defined in the sense that the geometry of the set of interpolation points does not differ too much from the ideal one. The algorithm is described in detail and some encouraging numerical results are presented.Graphical Abstract: [Figure not available: see fulltext.]

KW - Derivative-free optimization

KW - Equality constraints

KW - Trust funnel

KW - Trust-region method

U2 - 10.1007/s10589-014-9715-3

DO - 10.1007/s10589-014-9715-3

M3 - Article

AN - SCOPUS:84939967330

VL - 61

SP - 25

EP - 49

JO - Computational Optimization and Applications

JF - Computational Optimization and Applications

SN - 0926-6003

IS - 1

ER -

A derivative-free trust-funnel method for equality-constrained nonlinear optimization

Abstract

Keywords

Access to Document

Fingerprint

Research output

An Interior-Point Trust-Funnel Algorithm for Nonlinear Optimization

BFO, a trainable derivative-free Brute Force Optimizer for nonlinear bound-constrained optimization and equilibrium computations with continuous and discrete variables

Quasi-Newton updates with weighted secant equations

Projects

DFO: Derivative free numerical algorithms for optimization

ADALGOPT: ADALGOPT - Advanced algorithms in nonlinear optimization

Cite this