1,390 research outputs found
Scalable Positivity Preserving Model Reduction Using Linear Energy Functions
In this paper, we explore positivity preserving model reduction. The reduction is performed by truncating the states of the original system without balancing in the classical sense. This may result in conservatism, however, this way the physical meaning of the individual states is preserved. The reduced order models can be obtained using simple matrix operations or using distributed optimization methods. Therefore, the developed algorithms can be applied to sparse large-scale systems
On second-order cone positive systems
Internal positivity offers a computationally cheap certificate for external
(input-output) positivity of a linear time-invariant system. However, the
drawback with this certificate lies in its realization dependency. Firstly,
computing such a realization requires to find a polyhedral cone with a
potentially high number of extremal generators that lifts the dimension of the
state-space representation, significantly. Secondly, not all externally
positive systems posses an internally positive realization. Thirdly, in many
typical applications such as controller design, system identification and model
order reduction, internal positivity is not preserved. To overcome these
drawbacks, we present a tractable sufficient certificate of external positivity
based on second-order cones. This certificate does not require any special
state-space realization: if it succeeds with a possibly non-minimal
realization, then it will do so with any minimal realization. While there exist
systems where this certificate is also necessary, we also demonstrate how to
construct systems, where both second-order and polyhedral cones as well as
other certificates fail. Nonetheless, in contrast to other realization
independent certificates, the present one appears to be favourable in terms of
applicability and conservatism. Three applications are representatively
discussed to underline its potential. We show how the certificate can be used
to find externally positive approximations of nearly externally positive
systems and demonstrated that this may help to reduce system identification
errors. The same algorithm is used then to design state-feedback controllers
that provide closed-loop external positivity, a common approach to avoid over-
and undershooting of the step response. Lastly, we present modifications to
generalized balanced truncation such that external positivity is preserved
where our certificate applies
Physics-based passivity-preserving parameterized model order reduction for PEEC circuit analysis
The decrease of integrated circuit feature size and the increase of operating frequencies require 3-D electromagnetic methods, such as the partial element equivalent circuit (PEEC) method, for the analysis and design of high-speed circuits. Very large systems of equations are often produced by 3-D electromagnetic methods, and model order reduction (MOR) methods have proven to be very effective in combating such high complexity. During the circuit synthesis of large-scale digital or analog applications, it is important to predict the response of the circuit under study as a function of design parameters such as geometrical and substrate features. Traditional MOR techniques perform order reduction only with respect to frequency, and therefore the computation of a new electromagnetic model and the corresponding reduced model are needed each time a design parameter is modified, reducing the CPU efficiency. Parameterized model order reduction (PMOR) methods become necessary to reduce large systems of equations with respect to frequency and other design parameters of the circuit, such as geometrical layout or substrate characteristics. We propose a novel PMOR technique applicable to PEEC analysis which is based on a parameterization process of matrices generated by the PEEC method and the projection subspace generated by a passivity-preserving MOR method. The proposed PMOR technique guarantees overall stability and passivity of parameterized reduced order models over a user-defined range of design parameter values. Pertinent numerical examples validate the proposed PMOR approach
On Projection-Based Model Reduction of Biochemical Networks-- Part I: The Deterministic Case
This paper addresses the problem of model reduction for dynamical system
models that describe biochemical reaction networks. Inherent in such models are
properties such as stability, positivity and network structure. Ideally these
properties should be preserved by model reduction procedures, although
traditional projection based approaches struggle to do this. We propose a
projection based model reduction algorithm which uses generalised block
diagonal Gramians to preserve structure and positivity. Two algorithms are
presented, one provides more accurate reduced order models, the second provides
easier to simulate reduced order models. The results are illustrated through
numerical examples.Comment: Submitted to 53rd IEEE CD
Balanced Truncation of k-Positive Systems
This paper considers balanced truncation of discrete-time Hankel -positive systems, characterized by Hankel matrices whose minors up to order k are nonnegative. Our main result shows that if the truncated system has order or less, then it is Hankel totally positive (-positive), meaning that it is a sum of first order lags. This result can be understood as a bridge between two known results: the property that the first-order truncation of a positive system is positive (), and the property that balanced truncation preserves state-space symmetry. It provides a broad class of systems where balanced truncation is guaranteed to result in a minimal internally positive system.T
We would like to thank the anonymous reviewers for their
outstanding support. The research leading to these results was
completed while the first author was a postdoctoral research
associate at the University of Cambridge. The research has
received funding from the European Research Council under
the Advanced ERC Grant Agreement Switchlet n.67
Variational cross-validation of slow dynamical modes in molecular kinetics
Markov state models (MSMs) are a widely used method for approximating the
eigenspectrum of the molecular dynamics propagator, yielding insight into the
long-timescale statistical kinetics and slow dynamical modes of biomolecular
systems. However, the lack of a unified theoretical framework for choosing
between alternative models has hampered progress, especially for non-experts
applying these methods to novel biological systems. Here, we consider
cross-validation with a new objective function for estimators of these slow
dynamical modes, a generalized matrix Rayleigh quotient (GMRQ), which measures
the ability of a rank- projection operator to capture the slow subspace of
the system. It is shown that a variational theorem bounds the GMRQ from above
by the sum of the first eigenvalues of the system's propagator, but that
this bound can be violated when the requisite matrix elements are estimated
subject to statistical uncertainty. This overfitting can be detected and
avoided through cross-validation. These result make it possible to construct
Markov state models for protein dynamics in a way that appropriately captures
the tradeoff between systematic and statistical errors
Rank Reduction with Convex Constraints
This thesis addresses problems which require low-rank solutions under convex constraints. In particular, the focus lies on model reduction of positive systems, as well as finite dimensional optimization problems that are convex, apart from a low-rank constraint. Traditional model reduction techniques try to minimize the error between the original and the reduced system. Typically, the resulting reduced models, however, no longer fulfill physically meaningful constraints. This thesis considers the problem of model reduction with internal and external positivity constraints. Both problems are solved by means of balanced truncation. While internal positivity is shown to be preserved by a symmetry property; external positivity preservation is accomplished by deriving a modified balancing approach based on ellipsoidal cone invariance.In essence, positivity preserving model reduction attempts to find an infinite dimensional low-rank approximation that preserves nonnegativity, as well as Hankel structure. Due to the non-convexity of the low-rank constraint, this problem is even challenging in a finite dimensional setting. In addition to model reduction, the present work also considers such finite dimensional low-rank optimization problems with convex constraints. These problems frequently appear in applications such as image compression, multivariate linear regression, matrix completion and many more. The main idea of this thesis is to derive the largest convex minorizers of rank-constrained unitarily invariant norms. These minorizers can be used to construct optimal convex relaxations for the original non-convex problem. Unlike other methods such as nuclear norm regularization, this approach benefits from having verifiable a posterior conditions for which a solution to the convex relaxation and the corresponding non-convex problem coincide. It is shown that this applies to various numerical examples of well-known low-rank optimization problems. In particular, the proposed convex relaxation performs significantly better than nuclear norm regularization. Moreover, it can be observed that a careful choice among the proposed convex relaxations may have a tremendous positive impact on matrix completion. Computational tractability of the proposed approach is accomplished in two ways. First, the considered relaxations are shown to be representable by semi-definite programs. Second, it is shown how to compute the proximal mappings, for both, the convex relaxations, as well as the non-convex problem. This makes it possible to apply first order method such as so-called Douglas-Rachford splitting. In addition to the convex case, where global convergence of this algorithm is guaranteed, conditions for local convergence in the non-convex setting are presented. Finally, it is shown that the findings of this thesis also extend to the general class of so-called atomic norms that allow us to cover other non-convex constraints
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