Complexity of model testing for dynamical systems with toric steady states

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Complexity of model testing for dynamical systems with toric steady states. / Adamer, Michael F.; Helmer, Martin.

In: Advances in Applied Mathematics, Vol. 110, 2019, p. 42-75.

Research output: Contribution to journalJournal articleResearchpeer-review

Harvard

Adamer, MF & Helmer, M 2019, 'Complexity of model testing for dynamical systems with toric steady states', Advances in Applied Mathematics, vol. 110, pp. 42-75. https://doi.org/10.1016/j.aam.2019.06.001

APA

Adamer, M. F., & Helmer, M. (2019). Complexity of model testing for dynamical systems with toric steady states. Advances in Applied Mathematics, 110, 42-75. https://doi.org/10.1016/j.aam.2019.06.001

Vancouver

Adamer MF, Helmer M. Complexity of model testing for dynamical systems with toric steady states. Advances in Applied Mathematics. 2019;110:42-75. https://doi.org/10.1016/j.aam.2019.06.001

Author

Adamer, Michael F. ; Helmer, Martin. / Complexity of model testing for dynamical systems with toric steady states. In: Advances in Applied Mathematics. 2019 ; Vol. 110. pp. 42-75.

Bibtex

@article{fb1563e8dbef47be96a5dd593d971376,
title = "Complexity of model testing for dynamical systems with toric steady states",
abstract = "In this paper we investigate the complexity of model selection and model testing for dynamical systems with toric steady states. Such systems frequently arise in the study of chemical reaction networks. We do this by formulating these tasks as a constrained optimization problem in Euclidean space. This optimization problem is known as a Euclidean distance problem; the complexity of solving this problem is measured by an invariant called the Euclidean distance (ED) degree. We determine closed-form expressions for the ED degree of the steady states of several families of chemical reaction networks with toric steady states and arbitrarily many reactions. To illustrate the utility of this work we show how the ED degree can be used as a tool for estimating the computational cost of solving the model testing and model selection problems.",
author = "Adamer, {Michael F.} and Martin Helmer",
year = "2019",
doi = "10.1016/j.aam.2019.06.001",
language = "English",
volume = "110",
pages = "42--75",
journal = "Advances in Applied Mathematics",
issn = "0196-8858",
publisher = "Academic Press",

}

RIS

TY - JOUR

T1 - Complexity of model testing for dynamical systems with toric steady states

AU - Adamer, Michael F.

AU - Helmer, Martin

PY - 2019

Y1 - 2019

N2 - In this paper we investigate the complexity of model selection and model testing for dynamical systems with toric steady states. Such systems frequently arise in the study of chemical reaction networks. We do this by formulating these tasks as a constrained optimization problem in Euclidean space. This optimization problem is known as a Euclidean distance problem; the complexity of solving this problem is measured by an invariant called the Euclidean distance (ED) degree. We determine closed-form expressions for the ED degree of the steady states of several families of chemical reaction networks with toric steady states and arbitrarily many reactions. To illustrate the utility of this work we show how the ED degree can be used as a tool for estimating the computational cost of solving the model testing and model selection problems.

AB - In this paper we investigate the complexity of model selection and model testing for dynamical systems with toric steady states. Such systems frequently arise in the study of chemical reaction networks. We do this by formulating these tasks as a constrained optimization problem in Euclidean space. This optimization problem is known as a Euclidean distance problem; the complexity of solving this problem is measured by an invariant called the Euclidean distance (ED) degree. We determine closed-form expressions for the ED degree of the steady states of several families of chemical reaction networks with toric steady states and arbitrarily many reactions. To illustrate the utility of this work we show how the ED degree can be used as a tool for estimating the computational cost of solving the model testing and model selection problems.

UR - http://www.scopus.com/inward/record.url?scp=85067341290&partnerID=8YFLogxK

U2 - 10.1016/j.aam.2019.06.001

DO - 10.1016/j.aam.2019.06.001

M3 - Journal article

VL - 110

SP - 42

EP - 75

JO - Advances in Applied Mathematics

JF - Advances in Applied Mathematics

SN - 0196-8858

ER -

ID: 222971860