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Cluster Gauss-Newton method : An algorithm for finding multiple approximate minimisers of nonlinear least squares problems with applications to parameter estimation of pharmacokinetic models

Aoki, Yasunori, 1982- (författare)
Uppsala universitet,Institutionen för farmaceutisk biovetenskap,Natl Inst Informat, Chiyoda Ku, 2-1-2 Hitotsubashi, Tokyo 1018430, Japan.;RIKEN, Sugiyama Lab, Baton Zone Program, Tsurumi Ku, 1-7-22 Suehiro Cho, Yokohama, Kanagawa 2300045, Japan
Hayami, Ken (författare)
Grad Univ Adv Studies SOKENDAI, Natl Inst Informat, Chiyoda Ku, 2-1-2 Hitotsubashi, Tokyo 1018430, Japan
Toshimoto, Kota (författare)
RIKEN, Sugiyama Lab, Baton Zone Program, Tsurumi Ku, 1-7-22 Suehiro Cho, Yokohama, Kanagawa 2300045, Japan
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Sugiyama, Yuichi (författare)
RIKEN, Sugiyama Lab, Baton Zone Program, Tsurumi Ku, 1-7-22 Suehiro Cho, Yokohama, Kanagawa 2300045, Japan
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 (creator_code:org_t)
2020-10-28
2022
Engelska.
Ingår i: Optimization and Engineering. - : Springer. - 1389-4420 .- 1573-2924. ; 23:1, s. 169-199
  • Tidskriftsartikel (refereegranskat)
Abstract Ämnesord
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  • Parameter estimation problems of mathematical models can often be formulated as nonlinear least squares problems. Typically these problems are solved numerically using iterative methods. The local minimiser obtained using these iterative methods usually depends on the choice of the initial iterate. Thus, the estimated parameter and subsequent analyses using it depend on the choice of the initial iterate. One way to reduce the analysis bias due to the choice of the initial iterate is to repeat the algorithm from multiple initial iterates (i.e. use a multi-start method). However, the procedure can be computationally intensive and is not always used in practice. To overcome this problem, we propose the Cluster Gauss-Newton (CGN) method, an efficient algorithm for finding multiple approximate minimisers of nonlinear-least squares problems. CGN simultaneously solves the nonlinear least squares problem from multiple initial iterates. Then, CGN iteratively improves the approximations from these initial iterates similarly to the Gauss-Newton method. However, it uses a global linear approximation instead of the Jacobian. The global linear approximations are computed collectively among all the iterates to minimise the computational cost associated with the evaluation of the mathematical model. We use physiologically based pharmacokinetic (PBPK) models used in pharmaceutical drug development to demonstrate its use and show that CGN is computationally more efficient and more robust against local minima compared to the standard Levenberg-Marquardt method, as well as state-of-the art multi-start and derivative-free methods.

Ämnesord

NATURVETENSKAP  -- Matematik -- Beräkningsmatematik (hsv//swe)
NATURAL SCIENCES  -- Mathematics -- Computational Mathematics (hsv//eng)

Nyckelord

Nonlinear least squares problem
Multi-start method
Cluster Newton method
Derivative-free method
Physiologically based pharmacokinetic (PBPK) model
Parameter estimation

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Av författaren/redakt...
Aoki, Yasunori, ...
Hayami, Ken
Toshimoto, Kota
Sugiyama, Yuichi
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NATURVETENSKAP
NATURVETENSKAP
och Matematik
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Optimization and ...
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Uppsala universitet

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