18,469 research outputs found
Model reduction of biochemical reactions networks by tropical analysis methods
We discuss a method of approximate model reduction for networks of
biochemical reactions. This method can be applied to networks with polynomial
or rational reaction rates and whose parameters are given by their orders of
magnitude. In order to obtain reduced models we solve the problem of tropical
equilibration that is a system of equations in max-plus algebra. In the case of
networks with nonlinear fast cycles we have to solve the problem of tropical
equilibration at least twice, once for the initial system and a second time for
an extended system obtained by adding to the initial system the differential
equations satisfied by the conservation laws of the fast subsystem. The two
steps can be reiterated until the fast subsystem has no conservation laws
different from the ones of the full model. Our method can be used for formal
model reduction in computational systems biology
Stochastic Nonlinear Model Predictive Control with Efficient Sample Approximation of Chance Constraints
This paper presents a stochastic model predictive control approach for
nonlinear systems subject to time-invariant probabilistic uncertainties in
model parameters and initial conditions. The stochastic optimal control problem
entails a cost function in terms of expected values and higher moments of the
states, and chance constraints that ensure probabilistic constraint
satisfaction. The generalized polynomial chaos framework is used to propagate
the time-invariant stochastic uncertainties through the nonlinear system
dynamics, and to efficiently sample from the probability densities of the
states to approximate the satisfaction probability of the chance constraints.
To increase computational efficiency by avoiding excessive sampling, a
statistical analysis is proposed to systematically determine a-priori the least
conservative constraint tightening required at a given sample size to guarantee
a desired feasibility probability of the sample-approximated chance constraint
optimization problem. In addition, a method is presented for sample-based
approximation of the analytic gradients of the chance constraints, which
increases the optimization efficiency significantly. The proposed stochastic
nonlinear model predictive control approach is applicable to a broad class of
nonlinear systems with the sufficient condition that each term is analytic with
respect to the states, and separable with respect to the inputs, states and
parameters. The closed-loop performance of the proposed approach is evaluated
using the Williams-Otto reactor with seven states, and ten uncertain parameters
and initial conditions. The results demonstrate the efficiency of the approach
for real-time stochastic model predictive control and its capability to
systematically account for probabilistic uncertainties in contrast to a
nonlinear model predictive control approaches.Comment: Submitted to Journal of Process Contro
Numerical Analysis
Acknowledgements: This article will appear in the forthcoming Princeton Companion to Mathematics, edited by Timothy Gowers with June Barrow-Green, to be published by Princeton University Press.\ud
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In preparing this essay I have benefitted from the advice of many colleagues who corrected a number of errors of fact and emphasis. I have not always followed their advice, however, preferring as one friend put it, to "put my head above the parapet". So I must take full responsibility for errors and omissions here.\ud
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With thanks to: Aurelio Arranz, Alexander Barnett, Carl de Boor, David Bindel, Jean-Marc Blanc, Mike Bochev, Folkmar Bornemann, Richard Brent, Martin Campbell-Kelly, Sam Clark, Tim Davis, Iain Duff, Stan Eisenstat, Don Estep, Janice Giudice, Gene Golub, Nick Gould, Tim Gowers, Anne Greenbaum, Leslie Greengard, Martin Gutknecht, Raphael Hauser, Des Higham, Nick Higham, Ilse Ipsen, Arieh Iserles, David Kincaid, Louis Komzsik, David Knezevic, Dirk Laurie, Randy LeVeque, Bill Morton, John C Nash, Michael Overton, Yoshio Oyanagi, Beresford Parlett, Linda Petzold, Bill Phillips, Mike Powell, Alex Prideaux, Siegfried Rump, Thomas Schmelzer, Thomas Sonar, Hans Stetter, Gil Strang, Endre Süli, Defeng Sun, Mike Sussman, Daniel Szyld, Garry Tee, Dmitry Vasilyev, Andy Wathen, Margaret Wright and Steve Wright
Parallel ADMM for robust quadratic optimal resource allocation problems
An alternating direction method of multipliers (ADMM) solver is described for
optimal resource allocation problems with separable convex quadratic costs and
constraints and linear coupling constraints. We describe a parallel
implementation of the solver on a graphics processing unit (GPU) using a
bespoke quartic function minimizer. An application to robust optimal energy
management in hybrid electric vehicles is described, and the results of
numerical simulations comparing the computation times of the parallel GPU
implementation with those of an equivalent serial implementation are presented
Robust and efficient solution of the drum problem via Nystrom approximation of the Fredholm determinant
The drum problem-finding the eigenvalues and eigenfunctions of the Laplacian
with Dirichlet boundary condition-has many applications, yet remains
challenging for general domains when high accuracy or high frequency is needed.
Boundary integral equations are appealing for large-scale problems, yet certain
difficulties have limited their use. We introduce two ideas to remedy this: 1)
We solve the resulting nonlinear eigenvalue problem using Boyd's method for
analytic root-finding applied to the Fredholm determinant. We show that this is
many times faster than the usual iterative minimization of a singular value. 2)
We fix the problem of spurious exterior resonances via a combined field
representation. This also provides the first robust boundary integral
eigenvalue method for non-simply-connected domains. We implement the new method
in two dimensions using spectrally accurate Nystrom product quadrature. We
prove exponential convergence of the determinant at roots for domains with
analytic boundary. We demonstrate 13-digit accuracy, and improved efficiency,
in a variety of domain shapes including ones with strong exterior resonances.Comment: 21 pages, 7 figures, submitted to SIAM Journal of Numerical Analysis.
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