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

**A two-dimensional search for a Gauss-Newton algorithm.** / Forbes, A.B.; Bartholomew-Biggs, M.

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

Forbes, AB & Bartholomew-Biggs, M 2009, 'A two-dimensional search for a Gauss-Newton algorithm', *Advanced Modeling and Optimization*, vol. 11, no. 4, pp. 435-447.

Forbes, A. B., & Bartholomew-Biggs, M. (2009). A two-dimensional search for a Gauss-Newton algorithm. *Advanced Modeling and Optimization*, *11*(4), 435-447.

Forbes AB, Bartholomew-Biggs M. A two-dimensional search for a Gauss-Newton algorithm. Advanced Modeling and Optimization. 2009;11(4):435-447.

@article{fae04e7f4a814acb96cc1c8634529903,

title = "A two-dimensional search for a Gauss-Newton algorithm",

abstract = "This paper describes a fall-back procedure for use with the Gauss-Newton method for nonlinear least-squares problems. While the basic Gauss-Newton algorithm is often successful, it is well-known that it can sometimes generate poor search directions and exhibit slow convergence. For dealing with such situations we suggest a new two-dimensional search strategy. Numerical experiments indicate that the proposed technique can be effective.",

author = "A.B. Forbes and M. Bartholomew-Biggs",

note = "Original article can be found at: http://www.ici.ro/camo/journal/jamo.htm",

year = "2009",

language = "English",

volume = "11",

pages = "435--447",

journal = "Advanced Modeling and Optimization",

issn = "1841-4311",

number = "4",

}

TY - JOUR

T1 - A two-dimensional search for a Gauss-Newton algorithm

AU - Forbes, A.B.

AU - Bartholomew-Biggs, M.

N1 - Original article can be found at: http://www.ici.ro/camo/journal/jamo.htm

PY - 2009

Y1 - 2009

N2 - This paper describes a fall-back procedure for use with the Gauss-Newton method for nonlinear least-squares problems. While the basic Gauss-Newton algorithm is often successful, it is well-known that it can sometimes generate poor search directions and exhibit slow convergence. For dealing with such situations we suggest a new two-dimensional search strategy. Numerical experiments indicate that the proposed technique can be effective.

AB - This paper describes a fall-back procedure for use with the Gauss-Newton method for nonlinear least-squares problems. While the basic Gauss-Newton algorithm is often successful, it is well-known that it can sometimes generate poor search directions and exhibit slow convergence. For dealing with such situations we suggest a new two-dimensional search strategy. Numerical experiments indicate that the proposed technique can be effective.

M3 - Article

VL - 11

SP - 435

EP - 447

JO - Advanced Modeling and Optimization

JF - Advanced Modeling and Optimization

SN - 1841-4311

IS - 4

ER -