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(Reference retrieved automatically from Web of Science through information on FAPESP grant and its corresponding number as mentioned in the publication by the authors.)

Applying the pattern search implicit filtering algorithm for solving a noisy problem of parameter identification

Full text
Author(s):
Diniz-Ehrhardt, M. A. [1] ; Ferreira, D. G. [1] ; Santos, S. A. [1]
Total Authors: 3
Affiliation:
[1] Univ Estadual Campinas, Inst Math, Rua Sergio Buarque de Holanda 651, BR-13083859 Campinas, SP - Brazil
Total Affiliations: 1
Document type: Journal article
Source: COMPUTATIONAL OPTIMIZATION AND APPLICATIONS; v. 76, n. 3, SI, p. 835-866, JUL 2020.
Web of Science Citations: 2
Abstract

Our contribution in this paper is twofold. First, the global convergence analysis of the recently proposed pattern search implicit filtering algorithm (PSIFA), aimed at linearly constrained noisy minimization problems, is revisited to address more general locally Lipschitz objective functions corrupted by noise. Second, PSIFA is applied for solving the damped harmonic oscillator parameter identification problem. This problem can be formulated as a linearly constrained optimization problem, for which the constraints are related to the features of the damping. Such a formulation rests upon a very expensive objective function whose evaluation comprises the numerical solution of an ordinary differential equation (ODE), with intrinsic numerical noise. Computational experimentation encompasses distinct choices for the ODE solvers, and a comparative analysis of the most effective options against the pattern search and the implicit filtering algorithms. (AU)

FAPESP's process: 13/07375-0 - CeMEAI - Center for Mathematical Sciences Applied to Industry
Grantee:Francisco Louzada Neto
Support Opportunities: Research Grants - Research, Innovation and Dissemination Centers - RIDC
FAPESP's process: 18/24293-0 - Computational methods in optimization
Grantee:Sandra Augusta Santos
Support Opportunities: Research Projects - Thematic Grants
FAPESP's process: 13/12964-4 - Derivative-free methods for nonlinear programming: constrained and noisy problems
Grantee:Deise Gonçalves Ferreira
Support Opportunities: Scholarships in Brazil - Doctorate