Trigonometric dynamical r-matrices over Poisson Lie base

Trigonometric dynamical r-matrices over Poisson Lie base
Trigonometric dynamical r-matrices over Poisson Lie base

a r X i v :m a t h /0403207v 1 [m a t h .Q A ] 12 M a r 2004Trigonometric dynamical r-matrices over Poisson Lie base ?A.Mudrov Department of Mathematics,Bar Ilan University,52900Ramat Gan,Israel,Max-Planck Institut f¨u r Mathematik,Vivatsgasse 7,D-53111Bonn,Germany.e-mail:mudrov@mpim-bonn.mpg.de Abstract Let g be a ?nite dimensional complex Lie algebra and l ?g a Lie subalgebra equipped with the structure of a factorizable quasitriangular Lie bialgebra.Consider the Lie group Exp l with the Semenov-Tjan-Shansky Poisson bracket as a Poisson Lie manifold for the double Lie bialgebra Dl .Let N l (0)?l be an open domain parameterizing a neighborhood of the identity in Exp l by the exponential map.We present dynamical r -matrices with values in g ∧g over the Poisson Lie base manifold N l (0).Key words

?

This research is supported in part by the Israel Academy of Sciences grant no.8007/99-03,the Emmy Noether Research Institute for Mathematics,the Minerva Foundation of Germany,the Excellency Center ”Group Theoretic Methods in the study of Algebraic Varieties”of the Israel Science foundation,and by the RFBR grant no.03-01-00593.

A particular case of the PL version of the CDYBE for a quasitriangular Lie bialgebra l=g and L being the group space Exp g appeared in[BFP]in connection with a factorization problem in the chiral WZW model.The corresponding dynamical r-matrix was written out in[FM].In the present paper we give examples of dynamical r-matrices for the quadruple (g,l,l?,L),where g is a complex Lie algebra and(l,l?)a factorizable quasitriangular Lie bialgebra.As a base manifold L we take a domain N l(0)in l parameterizing a neighborhood of the identity in the group Exp l equipped with the Semenov-Tjan-Shansky(STS)Poisson bracket,[S].

We apply the Etingof-Varchenko approach of base reduction,[EV],to the PL CDYBE. Then we employ,within the PL setting,the idea of Etingof-Schi?mann which is used for constructing generalized Alekseev-Meinrenken dynamical r-matrices in[ES].In this way we obtain PL dynamical r-matrices that are in between the r-matrices of[FM]and trigonometric r-matrices of[EV,S].

2Dynamical Yang-Baxter equation on PL base mani-fold

Let(l,l?)be a?nite dimensional Lie bialgebra and letδdenote the cobracket l→l∧l,[D]. Let Dl=l??l?op be the double Lie algebra with the canonical invariant symmetric tensor θ:= i(ηi?ξi+ξi?ηi),where{ξi}?l and{ηi}?l?are the dual bases.Recall from [DM1]that a Dl-manifold is called an l-base manifold if the Casimir elementθgenerates the

zero bidi?erential operator via the action of Dl.It follows that a base manifold is equipped with a Dh-PL structure induced by the r-matrix iηi?ξi∈(Dl)?2via the action of Dl.

In the present paper,by a function on L we understand an analytical or meromorphic function.Let g be a Lie algebra containing l as a subalgebra.Let us call a function r:L→∧2g quasi-invariant if

ξ?r(λ)+[ξ?1+1?ξ,r(λ)]=?δ(ξ),ξ∈l,(1) whereδis the cobracket on l and?denotes the l-action on functions on L by vector?elds. If the Lie bialgebra l is coboundary,i.e.δ(ξ):=[ξ?1+1?ξ,r l]for some r l∈∧2l,then a function r:L→∧2g is quasi-invariant if and only if the function r′:=r+r l is invariant,i.e.

ξ?r′(λ)+[ξ?1+1?ξ,r′(λ)]=0,ξ∈l.(2) De?nition2.1.A quasi-invariant function r:L→∧2g is called a dynamical r-matrix over the base L if

Alt ξi??ηi r(λ) +CYB r(λ) =Z,(3)

i

where?ηis the vector?eld on L generated byη∈l?op,Z?(∧3g)g is an invariant element, and

CYB(A):=[A12,A13]+[A12,A23]+[A13,A23],A∈g?g,

Alt(B):=B123?B213+B231,B∈g?g?g.

Note that De?nition2.1di?ers from the de?nition given in[DM1,DM2]by the change of sign r→?r.Sometimes we will use the term PL dynamical r-matrix in order to distinguish it from the particular case of abelian l?.

Remark2.2.Suppose(l,l?)is the trivial bialgebra,with zeroδ(abelian l?).Then replace-ment Z by?2Z results in the transformation r(λ)→?r(?λ)of the dynamical r-matrix,the solution to(3).

Equation(3)appeared in[DM1,DM2]in connection with equivariant deformation quan-tization on G-spaces.Its particular case for l=g and L=Exp l was considered in an equivalent form in the paper[BFP]devoted to the chiral WZW model.

3Poisson Lie CDYBE on group manifold

Suppose that the Lie bialgebra l is quasitriangular,i.e.coboundary with an r-matrix r l∈∧2l and an element?l∈(S2l)l such that CYB(r l+1

dt f(ge tξ)|0,ξr f(g)=

d

2 ?l l(η)+?r l(η)

forη∈l?op.(4)

Here and further on the elements from l?l are considered as linear operators from l?to l via pairing with the?rst tensor component.

From now on we assume the Lie bialgebra(l,l?)to be factorizable.This means that the Casimir element?l de?nes an isomorphism l?→l of l-modules.Let{ξi}?l be an orthonormal base with respect to the form??1

l

.Then the following proposition holds.

Proposition3.1.The quasi-invariant function r:L→∧2g is a dynamical r-matrix if and only if the invariant function r′:=r+r l satis?es the equation

Alt ξi??′ξi r′(λ) +CYB r′(λ) =Z?Z l,(5) i

where Z l:=CYB(r l)and the vector?eld?′ξis de?ned to be?′ξ:=1

coth ?

2

[?12g,?23g].

4

We may assume g to be reductive and extend this solution to a meromorphic function on h using projection along the center of g.

5Base reduction in trigonometric case

It is shown in[EV]that the dynamical Yang-Baxter r-matrix admits a reduction of base when l is a reductive Lie algebra with the trivial Lie bialgebra structure(abelian l?).We will show that an analogous statement also holds if l is a factorizable Lie bialgebra.

Let l be a reductive subalgebra of a complex Lie algebra g and let h l be its Cartan subalgebra.Suppose that l is equipped with a factorizable quasitriangular Lie bialgebra structure.Denote by r l?∧2l its classical r-matrix and by?l∈(S2l)l the corresponding Casimir element.Put the l-base manifold L to be N l(0)?l equipped with the STS Poisson bracket.Let h l denote the Cartan subalgebra of l.The open domain N h

l

(0):=N l(0)∩h l?h l is a base manifold for h l.

Theorem5.1.A quasi-invariant function r:N l(0)→∧2g is a dynamical r-matrix over

N l(0)if and only if the function?r(λ):=r|h

l (λ)+ρ(λ)+r l,whereρ(λ)=ρ(g,1,λ),λ∈N h

l

(0),

is a dynamical r-matrix over the abelian base h l.

Proof.Equation(3)on the quasi-invariant function r is equivalent to equation(5)on the invariant function r′:=r+r l.We assume in(5)that the orthonormal base{ξk}is compatible with the root decomposition.Equation(5)gives rise to the following equation for the restriction of r′to h l:

rk l

i=1

Alt x i??′x i r′(λ) + α∈?(l)Alt eα??′e?αr′(λ) +CYB r′(λ) =Z g?CYB(r l)(8) whereλ∈h l.For allξ∈l the vector?elds?′ζ,on N l(0)read

?′ξf(λ)=?f(λ)(1

2

adλ ξ,

where?f denotes the di?erential of a function f.On the other hand,ξ?f(λ)=?f(λ)(adλ)ξ. This implies,by l-invariance of r′,

eα??′e

r′(λ)=coth 1

?x i

atλ∈h l.So equation(8)can be rewritten as rk l

i=1

Alt x i??i r′(λ) +Alt [ρ12+ρ13,r′23] +CYB r′(λ) =Z g?CYB(r l)

or,upon the substitution r′=?r?ρ,as

rk l

i=1

Alt x i??i?r(λ) ?rk l i=1Alt x i??iρ(λ) +

+Alt [ρ12+ρ13,?r23?ρ23] +CYB(?r?ρ)=Z g?CYB(r l).(9)

Sinceρis skew,we?nd CYB(?r?ρ)to be equal to

CYB(?r)+CYB(ρ)?([?r12,ρ13]+[?r12,ρ23]+[?r13,ρ23]+[ρ12,?r13]+[ρ12,?r23]+[ρ13,?r23])

=CYB(?r)+CYB(ρ)?Alt [ρ12+ρ13,?r23] .

Also,it easy to see that Alt [ρ12+ρ13,ρ23] =2CYB(ρ).Taking this into account,we rewrite (9)as

rk l

Alt x i??i?r(λ) +CYB(?r)?rk l i=1Alt x i??iρ(λ) ?CYB ρ(λ) =Z g?CYB(r l). i=1

We have CYB(r l)=?CYB(?l)=1

coth 12coth ?

2

coth ?

2

[?12g,?23g].

4

Proof.Since g is simple,the restriction of the matrix adλ∈End(g)to the invariant subspace l⊥is invertible forλbelonging to a dense open subset in N l(0).Henceλ→r(λ)is a correctly de?ned meromorphic function on N l(0).The function r+r l is l-invariant and +r l+ρ(l,1,λ)=ρ(g,?,λ)forλ∈h l.

r|h

l

6Generalized Feh′e r-Marshall dynamical r-matrices Let g be a?nite dimensional complex Lie algebra and B:g→g an automorphism of order n.Then g=⊕j∈Z/n Z g j,where g j:=ker(B?e2ipj/n).The Lie algebra g0acts on g j for all j.

Let g be equipped with an ad-and B-invariant form;denote by?g∈(S2g)g the corre-sponding Casimir element.According to[ES],there exists a dynamical r-matrix over the base l?for the trivial Lie bialgebra l(abelian l?).Under the identi?cation g?g?g??g?End(g), it is given by the invariant functionρ:N(l)→End(g),ρ(A)|g

j

=f j(ad A),with

f0(s)=1

2

coth 1

2

coth 1n ,j=0.(11)

This solution corresponds to Z g=1

2

coth 12coth ?

2

coth ?n ,j=0.(13) Let?l denote the Casimir element of the restriction of the invariant form to l.

Theorem6.1.The equivariant function r′

trig

satis?es equation(5)for Z?g=?2 4

[?12l,?23l].

Proof.The proof is an appropriate modi?cation of the proof of Theorem A.1of[ES].At the

?rst step one considers g=l⊕...⊕l,the direct sum of n-copies of a reductive Lie algebra l

and B the cyclic permutation of these copies.One proves that the function r′trig|h

l

+ρ,where ρ(λ):=ρ(l,1,λ)from(7)is a dynamical r-matrix over the h l for Z=Z?g.At?=1,this function coincides with the corresponding function from[ES],see the proof of Proposition A.1;for?=1cf.Remark2.2.By Theorem5.1,r′solves equation(5)for these speci?c g and B.The case of general g is derived from this one similarly to[ES].Namely,de?ne a map W:N l(0)→∧3g setting

W(A)=Alt ?′r′trig(A) +CYB r′trig(A) ?Z?g+1

For B=id,formulas(12-13)give the trigonometric PL dynamical r-matrix of Feh′e r-Marshal,[FM].

References

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Res.Lett.8(2001)157–170.

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[F]G.Felder:Conformal?eld theories and integrable models associated to elliptic curves,

Proc.ICM Zurich,(1994)1247–1255.

[FM]L.Feh′e r and I.Marshall:On a Poisson-Lie analogue of the classical dynamical Yang-Baxter equation for self dual Lie algebras,Lett.Math.Phys.62(2002)51–62.

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dynamical system动力学系统

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1. Introduction. A dynamical system is a phenomenon that changes with tim, for instance the position of a pendulum, the weather, the amount of predators and prey in a lake, et cetera. The traditional way of describing a dynamical system is to use a linear system of differential equations. In this case we have a pretty simple theory to solve the problem (see for instace part 8). A more realistic model however often leads to nonlinear systems of differential equations. In this case it is much more complicated to describe the behavios in the long run, but with help of computers and existing theories we can sometimes obtain the solution as an attractor to the system. In many other cases we will instead get bifurcations or chaos. Chaos means that it is hard (or impossible) to determine the long term behavior; small changes in indata gives dramatic changes in the long term behavior. Some attractors can be described as fractals, some particular self similar sets (a small part of the set has the same structure as the whole set). Such attractors are sometimes called strange attractors.

Scalable nonlinear dynamical systems for agent steering and crowd simulation

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1Introduction Modeling autonomous digital agents and simulating their behavior in virtual envi-ronments is becoming increasingly important in computer graphics.In virtual real-ity applications,for example,each agent interacts with other agents and the environ-ment,so complex real-time interactions are necessary to achieve non-trivial behav-ioral scenarios.Modern game applications require smart autonomous agents with varying degrees of intelligence to permit multiple levels of game complexity.Agent behaviors must allow for complex interactions,and they must be adaptive in terms of both time and space(continuous changes in the environment).Finally,the mod-eling approach should scale well with the complexity of the environment geometry, the number and intelligence of the agents,and the various agent-environment inter-actions. There have been several promising approaches towards achieving the above goal. Many of them,however,are restrictive in terms of their application domain.They do not scale well with the complexity of the environment.They do not model time explicitly.They do not guarantee that the desired behavior will always be exhib-ited.This paper presents an alternative:a scalable,adaptive,and mathematically rigorous approach to modeling complex low-level behaviors in real time. We employ nonlinear dynamical system theory,kinetic data structures,and har-monic functions in a novel three-layer approach to modeling autonomous agents in a virtual environment.The?rst layer consists of differential equations based on nonlinear dynamic system theory,modeling the low-level behavior of the au-tonomous agent in complex environments.In the second layer,the motions of the agents,obstacles,and targets are incorporated into a kinetic data structure,provid-ing an ef?cient,scalable approach for adapting an agent’s motion to its changing local environment.In the third layer,differential equations based on harmonic func-tions determine a global course of action for an agent,initializing the differential equations from the?rst layer,guiding the agent,and keeping it from getting stuck in local minima.We also discuss how hybrid systems concepts for global planning can capitalize on both our layered approach and the continuous,reactive nature of our agent steering. In the?rst layer,we characterize in a mathematically precise way the behavior of our agents in complex dynamic virtual environments.The agents exist in a real-time virtual environment consisting of obstacles,targets,and other agents.Depending on the application,agents reach one or multiple targets while avoiding obstacles; targets and obstacles can be stationary and/or moving.Further,the inclusion of time as a variable in our system makes the formulation ef?cient,natural and powerful compared to traditional AI approaches. Our agent modeling is based on the coupling of a set of nonlinear dynamical sys-

Adaptive Feedback Synchronization of a General Complex Dynamical Network With Delayed Nodes

IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS—II: EXPRESS BRIEFS, VOL. 55, NO. 2, FEBRUARY 2008
183
Adaptive Feedback Synchronization of a General Complex Dynamical Network With Delayed Nodes
Qunjiao Zhang, Junan Lu, Jinhu Lü, Senior Member, IEEE, and Chi K. Tse, Fellow, IEEE
Abstract—In the past decade, complex networks have attracted much attention from various ?elds of sciences and engineering. Synchronization is a typical collective behavior of complex networks that has been extensively investigated in recent years. To reveal the dynamical mechanism of synchronization in complex networks with time delays, a general complex dynamical network with delayed nodes is further studied. Based on a suitable model, we investigate the adaptive feedback synchronization and obtain several novel criteria for globally exponentially asymptotic synchronization. In particular, our hypotheses and the proposed adaptive controllers for network synchronization are very simple and can be readily applied in practical applications. Finally, numerical simulations are provided to illustrate the effectiveness of the proposed synchronization criteria. Index Terms—Adaptive feedback synchronization, complex networks, delayed nodes.
I. INTRODUCTION HE so-called complex network refers to a set of nodes connected by edges (graph) that has certain nontrivial topological features that are not found in simple networks [1]–[4]. Such nontrivial features involve a degree distribution with a heavy-tail, a hierarchical structure, a high clustering coef?cient, a community structure at different scales, and assortativity or disassortativity among vertices [2], [5]–[8]. It is well known that complex networks exist in many natural and man-made systems, e.g., food webs, neural networks, cellular and metabolic networks, electrical power grids, computer networks, technological networks, the World Wide Web, coauthorship and citation networks, social networks, etc. [1], [2]. Time delay inevitably exists in natural and man-made networks [9]–[15]. In much of the literature, time delays in the cou-
T
Manuscript received May 25, 2007; revised September 17, 2007. This work was supported by National Natural Science Foundation of China under Grants 60574045, 70771084, 60221301 and 60772158, by National Basic Research (973) Program of China under Grant 2007CB310800 and 2007CB310805, by Important Direction Project of Knowledge Innovation Program of Chinese Academy of Sciences under Grant KJCX3-SYW-S01, and by Scienti?c Research Startup Special Foundation on Excellent PhD Thesis, and Presidential Award of Chinese Academy of Sciences. This paper was recommended by Associate Editor J. Suykens. Q. Zhang and J. Lu are with the School of Mathematics and Statistics, Wuhan University, Wuhan 430072, China (e-mail: qunjiao99@https://www.360docs.net/doc/169701972.html,, jalu@https://www.360docs.net/doc/169701972.html,). J. Lü is with the Institute of Systems Science, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100080, China, and also with the State Key Laboratory of Software Engineering, Wuhan University, Wuhan 430072, China (e-mail: jhlu@https://www.360docs.net/doc/169701972.html,). C. K. Tse is with the Department of Electronic and Information Engineering, Hong Kong Polytechnic University, Hong Kong, China (e-mail: encktse@polyu. edu.hk). Digital Object Identi?er 10.1109/TCSII.2007.911813
plings (edges) are considered [9]–[11]; however, the time delays in the dynamical nodes [12]–[15], which are more complex, are still relatively unexplored. As a matter of fact, one can ?nd numerous examples in the real world which are characterized by delayed differential equations having time delays in the dynamical nodes [12]–[15]. For example, the delayed logistic differential equation, which has time delay in the dynamical node, is a representative dynamical model of the electrochemical intercalations and physiological systems [15]. It is thus imperative to further investigate complex dynamical networks with delayed nodes. However, such complex networks are still relatively unexplored due to their complexity and the absence of an appropriate simpli?cation procedure [9], [10]. Further, the lack of a general approach or tool to study such kind of complex networks has also obstructed the progress of development of their analysis [11]. Recently, we developed a method to deal with such kind of complex networks [16], and in this paper we further investigate the synchronization of a general complex dynamical network with delayed nodes. Synchronization is now widely regarded as a kind of collective behavior which is exhibited in many natural systems [1], [16], [17]. In essence, synchronization is a form of self-organization. It has been demonstrated that many real-world problems have close relationships with network synchronization [1], [2], [8]. For example, theoretical and experimental results show that a mammalian brain not only displays in its storage of associative memories, but also modulates oscillatory neuronal synchronization by selective perceive attention [18]. Recently, synchronization of complex dynamical networks has been a focus in various ?elds of science and engineering. Wu [5] investigated the synchronization of random directed networks. Lü and Chen [8] studied the synchronization of time-varying complex dynamical networks. Li et al. [9], [11] explored the synchronization of complex dynamical networks with nonlinear inner-coupling functions and time delays. Zhou et al. [16] studied the adaptive synchronization of an uncertain complex dynamical network. Sorrentino et al. [17] investigated the controllability of complex networks with pinning controllers. However, the important issue of synchronization of complex dynamical networks with delayed nodes has only been lightly covered [9]–[15]. This paper will further investigate the adaptive feedback synchronization of complex dynamical networks with delayed nodes. In particular, we obtain several novel criteria for globally exponentially asymptotic synchronization. It should be pointed out that our hypotheses and the proposed adaptive controllers for network synchronization are very simple and easy to apply. This paper is organized as follows. Section II introduces a general complex dynamical network with delayed nodes and several useful hypotheses. A set of novel adaptive feedback synchronization criteria are given in Section III. Section IV uses two
1549-7747/$25.00 ? 2008 IEEE

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Ergodic Theory, Analysis, and Efficient Simulation of Dynamical Systems Springer, 2000. Asp

Ergodic Theory,Analysis,and Ef?cient Simulation of Dynamical Systems Springer,2000.

2Becker,B¨u rkle,Happe,Preu?er,Rumpf,Spielberg,Strzodka which help to extract such features and to visualize them in an intuitively receptable way. Furthermore,two methods based on a modelling with partial differential equations are described which allow an easy perception of?ow data.The texture transport method especially applies to timedependent velocity?https://www.360docs.net/doc/169701972.html,grangian coordinates are computed solving the corresponding linear transport equations numerically.Choos-ing an appropriate texture on the reference frame the coordinate mapping can be used as a suitable texture mapping.Alternatively,the nonlinear diffusion methods serves as an appropriate scale space method for the visualization of complicated ?ow patterns.It is closely related to nonlinear diffusion methods in image analysis where images are smoothed while still retaining and enhancing edges.Here an ini-tial noisy image is smoothed along streamlines,whereas the image is sharpened in the orthogonal direction.The two methods have in common that they are based on a continuous model and discretized only in the?nal implementational step.There-fore,many important properties are naturally established already in the continuous model. Concerning invariant manifolds of dynamical systems,a novel visualization ap-proach is presented.It is based on research concerning ef?cient and robust set ori-ented computational methods,which were introduced by M.Dellnitz,T.Hohmann, and O.Junge[8,9].Thereby the manifolds are covered with leaf boxes of a binary tree of boxes.The visualization technique to be presented here allow an interactive manipulation and inspection of these sets and an accompanying invariant measure density.Furthermore to struggle out the local dynamics,a covering of the leaf boxes with a dense set of short integral lines is considered.These line segments can then be shaded and animated. To introduce the general topic,let us brie?y recall the principle setting of?ow vi-sualization.The visualization of?eld data,especially of velocity?elds from CFD computations is one of the fundamental tasks in scienti?c visualization.The sim-plest method to draw vector plots at nodes of some overlayed regular grid in general produces visual clutter,because of the typically different local scaling of the?eld in the spatial domain,which leads to disturbing multiple overlaps in certain regions, whereas in other areas small structures such as eddies can not be resolved ade-quately.The central goal is to obtain a denser,intuitively better receptible method. Furthermore it should be closely related to the mathematical meaning of?eld data, which is mainly expressed in its one to one relation to the corresponding?ow.Single particle lines only very partially enlighten features of a complex?ow?eld.Thus,we ask for an automatic selection procedure of interesting particle lines and features or alternatively a suitable dense pattern which represents the?ow globally on the computational domain. 2Iconic Visualization of Flow Phenomena Complex physical phenomena can be simulated and resolved with large scale com-putations based on recent numerical methods,in particular adaptive,time–dependent,

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