Accession Number:

ADA416288

Title:

Combined Linear and Nonlinear Modeling of Data

Descriptive Note:

Technical rept.

Corporate Author:

NAVAL UNDERSEA WARFARE CENTER DIV NEWPORT RI

Personal Author(s):

Report Date:

2003-04-28

Pagination or Media Count:

46.0

Abstract:

A method is presented for reducing the dimensionality of the search space when some of the unknown parameters appear linearly in the model fit. After elimination of the linear parameters, the gradient vector and the Hessian matrix of the resultant Hermitian form are derived so that an efficient minimization procedure can be developed in multiple dimensions. A destabilizing term is identified in the Hessian matrix and can be dropped from the calculations if desired. This approach is expected to be more reliable it also does not require any second-order partial derivatives, leading to fewer computations for finding the minimum in the multidimensional search space.

Subject Categories:

  • Operations Research

Distribution Statement:

APPROVED FOR PUBLIC RELEASE