Decentralized Estimation of the Algebraic Connectivity for Strongly Connected Networks
|
|
- Sandra Cole
- 5 years ago
- Views:
Transcription
1 Decentralized Estimation of the Algebraic Connectivity for Strongly Connected Networs Hasan A Poonawala and Mar W Spong Abstract The second smallest eigenvalue λ 2 (L) of the Laplacian L of a networ G is a parameter that captures important properties of the networ. Applications such as synchronization of networed systems, consensus-based algorithms and networ connectivity control may require one to regulate the magnitude of λ 2 (L) in order to achieve suitable networ performance. The problem of decentralized estimation of λ 2 (L) for directed graphs is thus a relevant problem, yet it has received little attention thus far. We present an algorithm for its estimation and demonstrate its performance. I. INTRODUCTION The eigenvalues of the graph Laplacian L(G) [] of a networ G contain important information regarding the networ s properties and performance. The second smallest eigenvalue λ 2 of L(G) is particularily significant. Note that for the case when L is asymmetric, the eigenvalues are ordered based on their absolute values. In the case of undirected networs, λ 2 (L) is called the Fiedler value [2]. The Fiedler value can be used to determine whether an undirected networ is connected or not. The value of λ 2 (L) for a directed networ can be used to deduce whether the networ has a globally reachable node [3] or not. Therefore, λ 2 (L) is called the algebraic connectivity of the networ. For both directed and undirected networs, the rate of convergence of several consensus and synchronization algorithms depends on the magnitude of λ 2 (L) [3], [4]. In the case of Multi-Robot Networs (MRN), the communication networ is state dependent and hence λ 2 (L) changes depending on the motion of the robots. This requires monitoring of λ 2 (L) and possibly the adoption of control strategies to influence its value. Thus estimation of λ 2 (L) is a relevant problem in several applications. Several methods to estimate the algebraic connectivity for undirected graphs have been proposed, using power iterations [5], decentralized orthogonal iterations [6], Fast Fourier Transforms [7] and dynamical systems theory [8]. The methods in [6] and [7] are computationally expensive. The third method is highly effective and has been implemented in experiments [9], [] where the objective is to preserve networ connectivity. The method in [5] estimates the th power of a matrix C derived from the Laplacian, using power iterations []. The largest eigenvalue of C can then be obtained from the limit of the th root of the induced Hasan A Poonawala is with the Eri Jonsson School of Engineering and Computer Science, University of Texas at Dallas, Richardson, Texas, USA hasanp@utdallas.edu Mar W Spong is the Dean of the Eri Jonsson School of Engineering and Computer Science, University of Texas at Dallas, Richardson, Texas, USA mspong@utdallas.edu infinity norm of C as increases. The estimate of the Fiedler value is then computed directly from the estimated largest eigenvalue of C. However, these methods do not apply to directed graphs, since in this case λ 2 (L) may be complex, and the methods above only return real values. The decentralized estimation of λ 2 (L) for directed networs has received little attention thus far. One method for its estimation is given in [2]. The method proposes the construction of a matrix A L similar to C in [5], however A L may not be symmetric. Again, λ 2 (L) can be computed directly from the largest eigenvalue of A L. Since A L may be asymmetric, the power iteration only yields an estimate of the absolute values of the elements of the eigenvector ν corresponding to the largest eigenvalue of A L. The arguments of the elements of ν are obtained by finding the root of a nonlinear function. The Jacobian of this function turns out to be singular on an uncountable set (see Appendix), which maes the numerical computation of the root unreliable, and in the authors experience unsuccessful. Another method was presented in [3], which can be used to obtain λ 2 (L) R even if λ 2 (L) is complex. In contrast, the method presented in this paper obtains the complex number λ 2 (L) and not just its modulus. Similar to [5], [2], the adjacency matrix A of the networ is converted into a matrix Q, whose largest eigenvalue is a function of λ 2 (L). A modified power iteration algorithm [4] can be used to compute the dominant eigenvalue of Q if it is complex. This algorithm combines a power iteration with the construction of a quadratic equation whose roots contain the largest eigenvalues of Q. Solving only a quadratic equation maes the method more reliable when compared to that in [2]. If the largest eigenvalue of Q is real, then standard power iterations can be used to estimate it. The main contribution of this paper is to identify an iterative algorithm that allows each node in a directed graph to obtain an estimate of λ 2 (L) through local communication only. The algorithm is shown to converge as the iterations proceed. A criterion is provided by which one can decide when to stop the iterations, while ensuring sufficient accuracy in the estimate. Examples are provided which show the performance of the estimation method. II. PRELIMINARIES In this section we introduce various definitions and notations from graph theory that will be used in this paper. A weighted directed graph G is a tuple consisting of a set of vertices V = {,..., n} (also called nodes) and a set of edges E. An edge ɛ is an ordered pair (i, j) which indicates
2 that a connection exists which starts at node j and ends at node i. Thus (i, j) is an in-edge of node i and an out-edge of node j. We can define the set of neighbors N i of agent i as N i = {j V (i, j) E} which has cardinality n i. We also define N = N i i. Each edge (i, j) E is associated with a strength w ij (, ], giving rise to the adjacency matrix A = {a ij } R n n, given by { w ij if (i, j) E a ij = () otherwise The Laplacian of the graph is then given by L = D A where D is a diagonal matrix whose i th diagonal element is n j= a ij. The Laplacian L always has an eigenvalue at, corresponding to a right eigenvector given by n n, where n R n, n = [,..., ] T. The second smallest eigenvalue of L is denoted by λ 2 (L). An undirected networ is connected if a path exists between any two nodes, which is equivalent to the Fiedler value being non-zero. For directed graphs, there are multiple notions of connectivity. The most relevant one is that of strong connectivity. A directed networ is strongly connected if there exists a directed path between any two nodes of the networ. We can now state the following properties [2]: λ 2 (L) has multiplicity if the graph is strongly connected λ j (L), j 2,..., n have positive real part if the graph is strongly connected The graph Laplacian can be converted into a rowstochastic and non-negative matrix S R n n, by using the transformation S = I n εl (2) where I n is the identity matrix of size n and ε > is a sufficiently small number. This was done in [5], with a view to analysis of discrete-time consensus protocols. It turns out that the same matrix is useful in the estimation of λ 2 (L). Assuming that each edge weight w ij is bounded above by, then selecting ε /n ensures S is non-negative. We now show that S is an irreducible matrix if G is strongly connected. Lemma II.. Consider a graph G with adjacency matrix A and matrix S R n n defined by (2). If G is strongly connected, then S is irreducible. Proof. If the graph G is strongly connected, A is an irreducible matrix [6]. The matrix S is of the form A + B where is a non-zero scalar and B is a diagonal matrix. Suppose that S is reducible. Then there exists a permutation matrix P R n n such that P T SP is in upper bloc The n th smallest and largest eigenvalues of a matrix A are denoted by λ n (A) and λ n(a) respectively triangular form. This implies that P T AP + P T BP is upper bloc triangular. Since B is diagonal, so is P T BP. Thus, P T AP must also be upper bloc triangular, which contradicts the fact that A is irreducible. Hence, S must be irreducible. The choice of ε /n also ensures that the diagonal elements are non-zero, implying that the trace of S is nonzero. Since S is irreducible and its trace is positive, it is a primitive matrix [7]. This fact is central to the success of the estimators presented in this paper. III. DECENTRALIZED ESTIMATION OF λ 2 (L) Consider a graph G with adjacency matrix A and graph Laplacian L. We want each node to estimate λ 2 (L). This estimation must be decentralized, meaning that each node may only receive information via communication with its neighbors. Another constraint is that each node only nows part of the matrices A and L. In particular, node i only nows the variables a ij for j {, 2,... n}. The estimation method presented in this paper requires the following assumption: A The graph G is strongly connected. As described in Section I, λ 2 (L) can be estimated by estimating the largest eigenvalue of a matrix Q using a modified power iteration method. The matrix Q is given by Q = S γ 2 γ γ T where S is the non-negative row stochastic matrix defined by (2), and γ is the left eigenvector of S corresponding to the eigenvalue such that γ T n =. If the eigenvalues of S are denoted by λ i (S) for i {, 2,..., n} such that λ i (S) λ i+ (S), then the eigenvalues of Q can be shown to be {, λ 2 (S), λ 3 (S),..., λ n (S)} ( [2], Lemma 4). Thus, λ (Q) = λ 2 (S), where λ (Q) is the largest eigenvalue of Q. The value of λ 2 (L) can be computed from λ 2 (S) using the relation λ 2 (L) = ε ( λ 2(S)) = ε ( λ (Q)) The power iteration method involves the computation of a vector z() R n given by z() = Q z(), where z() R n is some initial condition. This computation can be implemented in an iterative manner using the equation z( + ) = Qz() where z() is obtained at the th iteration. Three consecutive values z( + ), z() and z( ) can be used to compute λ 2 (S) = λ (Q), for sufficiently large. In a decentralized implementation of (5), node i computes the i th component of z( + ). This computation requires each node to now γ and z(). It can be shown [5] that for any primitive stochastic matrix S, lim l (S) l = n γ T (6) (3) (4) (5)
3 Suppose ω R n is a distributed vector, such that each node i holds the i th element ω i. The authors in [5] use (6) to design a consensus algorithm given by w i (l + ) = s i w (l) (7) N i where S = {s ij }. The authors show that lim l w i (l) = n i γ,iw i (), where γ,i is the i th element of γ. This readily implies a method to distribute the γ to all agents, and through γ any other vector v R m. In order to obtain γ, each agent implements n copies of (7), with the i th robot s initial value for the j th copy given by the ronecer delta product δ j i. Once γ is nown, any distributed n dimensional vector v can be completely spread to any node by implementing n copies of (7) with the i th robot s initial value for the j th copy given by v i δ j i. The estimate at each node converges to an n dimensional vector ω such that the i th element is v i γ,i. The vector v is obtained as Γ ω, where Γ = diag(γ ). This method can be used to spread z( + ) to all nodes, once z i ( + ) has been computed at each node. This procedure can also be found in [2]. With these details in place, we are ready to present an estimator that allows each node to estimate λ 2 (L) using communication with its neighbors only. This is the main result of this paper. Theorem III.. Consider a row-stochastic matrix S(G) corresponding to a strongly connected directed graph G, as defined in (2). Consider the following algorithm: Algorithm Let the i th node of the graph G perform the following iterative estimation scheme E Initialize an estimate z i () = (n) E2 Perform the iterations z i ( + ) = s ij z j () j N i (8) γ () 2 γ,i()(γ T z()) where γ and z() are obtained using estimators defined by (7) with appropriate initial conditions E3 Store the result of three consecutive iterations z p (), z p ( ) and z p ( 2) for p i N i E4 Solve the n i + linear equations z p () + bz p ( ) + cz p ( 2) = for b and c using the method of least squares, where p i N i. E5 Compute the roots x i, () and x i,2 () of the equation E6 Compute x 2 + bx + c = x i,3 () = z i ()/z i ( ) Then, as, at least one of the estimates x i,j () λ 2 (S), where j {, 2, 3}. Proof. When all n nodes implement steps S and S2, the resulting computation is the decentralized implementation of the power iteration step (5), as shown below: z( + ) = Sz() γ 2 γ γ T z() ( = S ) γ 2 γ γ T z() = Qz() We now study the form of z( + ) as increases. The eigenvectors v i of Q are identical to those of S [2], and are independent. Let the v i have indices such that their corresponding eigenvalues λ i are ordered as λ λ 2... λ n Hence, the initial condition z() for the power iteration can be expressed as z() = β () so that i= z() = Q z() = = β i λ i= β i Q v i i= (9) () Case : λ (Q) = λ 2 (Q) or λ (Q), λ 2 (Q) R Let b, c R be the coefficients of the monic second order polynomial whose roots are the two largest eigenvalues eigenvalues of Q (either both complex conjugates or both real numbers). Then λ 2 j + b λ j + c =, j {, 2} (2) Consider the sum f(b, c) = z + bz + cz 2. Using () we obtain f(b, c) = β i λ + b( β i λ ) + c( β i λ 2 ) = = i= i= i=j i= β i (λ 2 i + bλ i + c)λ 2 β i (λ 2 i + bλ i + c)λ 2 + ɛ i= (3) where ɛ = n i=j β i(λ 2 i + bλ i + c)λ 2, and j is the index of the largest eigenvalue such that λ j (Q) < λ 2 (Q). If λ (Q) is complex, then j = 3 because of A. Clearly, f(b, c ) = ɛ. Consider the solutions to f(b, c) =. The i th node computes this by solving the n i + linear equations z p () + [ z p ( ) z p ( 2) ] [ ] b = (4) c where p N i {i}. The relation f(b, c ) = ɛ at node i is equivalent to the n i + linear equations given by z p () + [ z p ( ) z p ( 2) ] [ ] b = ɛ (5) c,p
4 We can combine (4) and (5) to obtain the system of equations [ zp ( ) z p ( 2) ] [ ] b b = ɛ (6) c c,p Since λ 2 > λ 3, as, ɛ z. This implies that as, (b, c) (b, c ). Note that z( ) and z( 2) are independent, since λ (Q) is not real, and v 2 = v. Thus, the matrix formed from z( ) and z( 2) in (6) has ran 2. In turn, (x i,, x i,2 ) (λ (Q), λ (Q)) = (λ 2 (S), λ 2 (S)). Case 2: λ (Q) > λ 2 (Q) This case occurs when λ (Q) is real. The estimate can be expressed as x i,3 () = z i() (7) z i ( ) = β λ n v + i=2 β iλ β λ v + n i=2 β iλ Since λ > λ 2, the terms in the sum must become negligible compared to the first term, as. Then clearly lim This completes the proof. z i () z i ( ) = λ (8) Remar. The Fiedler value λ 2 (L) can now be obtained as ε ( λ 2(S)). Remar 2. The Theorem states that one of three estimates converges to the true value of λ 2 (S). We propose a stopping criterion in the next section which is used to extract the estimated value of λ 2 (S) from the sequences of values x i,j (). IV. NUMERICAL IMPLEMENTATION AND EXAMPLES An important issue that must be resolved before implementing the estimator in Section III is to decide at which iteration the estimation may be stopped, and one of the x i,j (), j {, 2, 3} be accepted as the estimate of λ 2 (S) for each node i. The authors in [2] discuss methods to ensure convergence of the estimates of γ and ω i. We now describe a method to detect when the convergence of one of the x i,j has occurred. Our stopping criterion relies on the observation that for large values of, the error in the estimate decreases according to a power law. For the case when λ 2 (S) is real, this property can be seen from Equation (7). Consider the case when λ 2 (S) is complex. We note that ɛ = i=3 β i (λ 2 i + bλ i + c)λ 2 which implies that for a sufficiently large, ɛ β i (λ 2 i + bλ i + c)λ 2 (9) (2) Equation (2) indicates that ɛ 2 decreases according to a power law, for sufficiently large. From (6), we see that the error in estimated values (b, c ) will follow this power law. We can model this behavior as b() b = λ b( ) ˆb c() c = λ c( ) ĉ (2) xi,, xi, Fig. : Absolute values of the estimates of λ 2 (S ) for each agent given by x i, and x i,2. The dashed blac line indicates λ 2 (S ). It is straightforward to solve for (ˆb, ĉ ) using (2), given the current estimates b() and c() and a small number of previous estimates. As estimates (b(), c()) converge to (b, c ), the predictions (ˆb, ĉ ) approach (b, c ). Thus, when the difference between the current estimate (b(), c()) at iteration and the predicted true value (ˆb, ĉ ) is smaller than some specified threshold, we can stop the iterations with the estimate at iteration (or predicted value) accepted as our final estimate. An additional condition is imposed that requires the difference between the predicted values at iteration and to be smaller than some threshold. We now describe the convergence of all three estimates x i,3. If λ 2 (S) is complex, then (x i,, x i,2 ) converge to (λ 2 (S), λ 2 (S)) but x i,3 does not converge to any number. When λ 2 (S) R and λ 3 (S) is complex, then x i,3 converges to λ 2 (S) but (x i,, x i,2 ) do not converge to any value. When λ 2 (S), λ 3 (S) R then (x i,, x i,2 ) converges to (λ 2 (S), λ 3 (S)) ( or (λ 3 (S), λ 2 (S))) and x i,3 converges to λ 2 (S). In order to demonstrate the performance of the algorithm, we implement the estimation algorithm on three rowstochastic matrices. The first was used as an example in [2], and is presented to enable a comparison with their wor. Example. Estimate λ 2 (S ), where.3.7 S = In Figure we can observe that the estimate has converged to the true value of.3 by the fourth iteration (only the absolute value is plotted for representation). The stopping criterion is satisfied at the fourth iteration for all nodes (see Figure 2). We continue the iterations in order to facilitate a comparison with Figure b in [2], wherein the estimates do not settle at the true value. It is possible to accept the mean of several iterations as the estimate, however we submit that our algorithm has better performance.
5 ˆbi, ĉi, b, c Fig. 2: The estimates of b and c for each node in Example. The dashed vertical line mars the iteration at which the stopping criterion is satisfied. The dashed horizontal blac lines are the true values. ˆbi, ĉi, b, c (a) The estimates of b and c for each node. The estimates and predictions converge as increases. The dashed vertical lines indicate the iteration at which the stopping criterion is met for each node. xi,, xi, (b) Absolute values of the estimates of λ 2(S 2) for each agent given by x i, and x i,2. The dashed blac line indicates λ 2(S 2). Fig. 3: Simulation results for the second example. Plots of the same color corresponds to the same node. The sets N and N 2 are identical, hence their estimates are identical and only four plots can be seen. xi, Fig. 4: The estimates x i,3 of λ 2 (S 2 ) using (7) for all five nodes. The sequence of estimates fail to meet the stopping criterion for any. Example 2. Estimate λ 2 (S 2 ), where S 2 = The matrix S 2 is generated by creating a random matrix in MATLAB. The matrix is scaled and the diagonal entries modified so that the matrix is row stochastic. Its second largest eigenvalue (in magnitude) is.733 ±.8893i. Figure 3 shows the results of the estimation. The estimates of b and c are seen to converge to their expected true values in Figure 3a. The dashed vertical lines mar the iterations at which the stopping criterion is met for the different nodes, which occurs when x i,2 is nearly equal to λ 2 (S 2 ). The absolute values of the estimates x i, and x i,2 are seen to converge to that of λ 2 (S 2 ) for all five nodes, in Figure 3b. As expected, the estimate x i,3 (which is always a real number) does not converge to λ 2 (S 2 ) since the latter is complex, as seen in Figure 4. Example 3. Estimate λ 2 (S 3 ), where S 3 = This matrix is generated in the same way as in the previous example. Its second largest eigenvalue is.54. Figure 5a shows the results of the estimation. The estimates x i,3 are seen to converge to λ 2 (S 3 ) for all five agents. Since λ 3 (S 3 ) is real, (x i,, x i,2 ) converges to (λ 2 (S 3 ), λ 3 (S 3 )). This can be seen in Figure 5b. Note that the stopping criterion is met at an earlier iteration for these estimates compared to the standard power iterations for real largest eigenvalues. Since each node is connected to all the remaining nodes, all five estimates are identical in Figure 5b.
6 xi, (a) Absolute values of x i,3 for each node. The dashed blac line denotes λ 2(S 2). ˆbi, ĉi, b, c xi,, xi, (b) The estimates of b and c for each node (above) and the resulting estimates x i, and x i,2 (below), for S 3. Fig. 5: Simulation results for the third example. V. CONCLUSION In this paper we propose a decentralized method to estimate λ 2 (L) of a strongly connected graph. The method combines existing decentralized methods to perform power iterations with a method to compute complex dominant eigenvalues of a real matrix. The algorithm requires each agent to communicate a vector whose size increases linearly with the number of agents in the networ. The computations performed by each agent at every iteration are inexpensive, unlie other methods. REFERENCES [] M. De Gennaro and A. Jadbabaie, Decentralized control of connectivity for multi-agent systems, in Decision and Control, 26 45th IEEE Conference on, Dec 26, pp [2] M. Fiedler, Algebraic connectivity of graphs, Czechoslova Mathematical Journal, vol. 23, no. 2, pp , 973. [3] C. Wu, Synchronization in Complex Networs of Nonlinear Dynamical Systems. World Scientific Publishing Company, Incorporated, 27. [Online]. Available: [4] R. Olfati-Saber, J. Fax, and R. Murray, Consensus and Cooperation in Networed Multi-Agent Systems, Proceedings of the IEEE, vol. 95, no., pp , 27. [5] R. Aragues, G. Shi, D. Dimarogonas, C. Sagues, and K. Johansson, Distributed algebraic connectivity estimation for adaptive eventtriggered consensus, in American Control Conference (ACC), 22, June 22, pp [6] D. Kempe and F. McSherry, A decentralized algorithm for spectral analysis, Journal of Computer and System Sciences, vol. 74, no., pp. 7 83, 28, ce:title Learning Theory 24 /ce:title. [7] M. Franceschelli, A. Gasparri, A. Giua, and C. Seatzu, Decentralized laplacian eigenvalues estimation for networed multi-agent systems, in Decision and Control, 29 held jointly with the 29 28th Chinese Control Conference. CDC/CCC 29. Proceedings of the 48th IEEE Conference on, 29, pp [8] P. Yang, R. Freeman, G. Gordon, K. Lynch, S. Srinivasa, and R. Suthanar, Decentralized estimation and control of graph connectivity in mobile sensor networs, in American Control Conference, 28, june 28, pp [9] A. C. Satici, H. A. Poonawala, H. Ecert, and M. W. Spong, Connectivity preserving formation control with collision avoidance for nonholonomic wheeled mobile robots, in International Conference on Intelligent Robots and Systems, to appear in Proceedings of, 23. [] L. Sabattini, N. Chopra, and C. Secchi, Decentralized connectivity maintenance for cooperative control of mobile robotic systems, vol. 32, no. 2, pp , October 23. [] G. H. Golub and C. F. Van Loan, Matrix computations (3rd ed.). Baltimore, MD, USA: Johns Hopins University Press. [2] C. Li and Z. Qu, Distributed estimation of algebraic connectivity of directed networs, Systems & Control Letters, vol. 62, no. 6, pp , 23. [3] F. Knorn, R. Stanojevic, M. Corless, and R. Shorten, A framewor for decentralised feedbac connectivity control with application to sensor networs, International Journal of Control, vol. 82, no., pp , 29. [Online]. Available: [4] A. Gourlay and G. Watson, Computational Methods for Matrix Eigenproblems. John Wiley & Sons, 973. [5] R. Olfati-Saber, J. Fax, and R. Murray, Consensus and cooperation in networed multi-agent systems, Proceedings of the IEEE, vol. 95, no., pp , Jan 27. [6] A. Berman and R. J. Plemmons, Nonnegative Matrices in the Mathematical Sciences. Society for Industrial Mathematics, Jan [7] H. Minc, Nonnegative matrices. Technion-Israel Institute of Technology, Dept. of Mathematics, 974. APPENDIX ANALYSIS OF JACOBIAN IN [2] The method in [2] requires one to find the values of θ 2, φ i and φ l which are roots of the equation f =, where f = [f, f, f 2 ] T and f is given by f (θ 2, φ i, φ l ) = ˆv 2,i( + )ˆv 2,l () ˆv 2,l ( + )ˆv 2,i () cos (θ 2 + φ i ) cos [( )θ 2 + φ l ] cos (θ 2 + φ l ) cos [( )θ 2 + φ i ] The determinant of its Jacobian f can be computed as det( f) = 8 cos θ 2 sec 2 (φ i + ( 3)θ 2 ) sec(φ i + ( 2)θ 2 ) sec(φ l + ( 2)θ 2 ) sec(φ i + ( )θ 2 ) sec(φ l + ( )θ 2 ) sec 2 (φ l + θ 2 ) sin 2 (φ i φ l ) sin 5 θ 2 (22) which vanishes at several points for any. Thus, the selection of an appropriate initial condition is critical to being able to find a root. Since the set of points where det( f) vanishes is large, the initial condition needs to be close to the solution, which is hard to achieve without prior nowledge.
Distributed Algebraic Connectivity Estimation for Adaptive Event-triggered Consensus
2012 American Control Conference Fairmont Queen Elizabeth, Montréal, Canada June 27-June 29, 2012 Distributed Algebraic Connectivity Estimation for Adaptive Event-triggered Consensus R. Aragues G. Shi
More informationDecentralized Connectivity Preserving Formation Control with Collision Avoidance for Nonholonomic Wheeled Mobile Robots
Decentralized Connectivity Preserving Formation Control with Collision Avoidance for Nonholonomic Wheeled Mobile Robots Hasan Poonawala, Ayut C Satici, Hazen Ecert and Mar W Spong Abstract The preservation
More informationarxiv: v1 [cs.ro] 8 Aug 2016
Enforcing Biconnectivity in Multi-robot Systems Mehran Zareh, Lorenzo Sabattini, and Cristian Secchi arxiv:1608.02286v1 [cs.ro] 8 Aug 2016 Abstract Connectivity maintenance is an essential task in multi-robot
More informationDistributed Control of the Laplacian Spectral Moments of a Network
Distributed Control of the Laplacian Spectral Moments of a Networ Victor M. Preciado, Michael M. Zavlanos, Ali Jadbabaie, and George J. Pappas Abstract It is well-nown that the eigenvalue spectrum of the
More informationResearch on Consistency Problem of Network Multi-agent Car System with State Predictor
International Core Journal of Engineering Vol. No.9 06 ISSN: 44-895 Research on Consistency Problem of Network Multi-agent Car System with State Predictor Yue Duan a, Linli Zhou b and Yue Wu c Institute
More informationOn the convergence of weighted-average consensus
On the convergence of weighted-average consensus Francisco Pedroche Miguel Rebollo Carlos Carrascosa Alberto Palomares arxiv:307.7562v [math.oc] 29 Jul 203 Abstract In this note we give sufficient conditions
More informationDiscrete-time Consensus Filters on Directed Switching Graphs
214 11th IEEE International Conference on Control & Automation (ICCA) June 18-2, 214. Taichung, Taiwan Discrete-time Consensus Filters on Directed Switching Graphs Shuai Li and Yi Guo Abstract We consider
More informationDecentralized Estimation of Laplacian Eigenvalues in Multi-Agent Systems
Decentralized Estimation of Laplacian Eigenvalues in Multi-Agent Systems Mauro Franceschelli a, Andrea Gasparri b, Alessandro Giua a, Carla Seatzu a. a Department of Electrical and Electronic Engineering,
More informationConsensus Protocols for Networks of Dynamic Agents
Consensus Protocols for Networks of Dynamic Agents Reza Olfati Saber Richard M. Murray Control and Dynamical Systems California Institute of Technology Pasadena, CA 91125 e-mail: {olfati,murray}@cds.caltech.edu
More informationFast Linear Iterations for Distributed Averaging 1
Fast Linear Iterations for Distributed Averaging 1 Lin Xiao Stephen Boyd Information Systems Laboratory, Stanford University Stanford, CA 943-91 lxiao@stanford.edu, boyd@stanford.edu Abstract We consider
More informationConsensus Problems on Small World Graphs: A Structural Study
Consensus Problems on Small World Graphs: A Structural Study Pedram Hovareshti and John S. Baras 1 Department of Electrical and Computer Engineering and the Institute for Systems Research, University of
More informationRobust Connectivity Analysis for Multi-Agent Systems
Robust Connectivity Analysis for Multi-Agent Systems Dimitris Boskos and Dimos V. Dimarogonas Abstract In this paper we provide a decentralized robust control approach, which guarantees that connectivity
More informationSystems & Control Letters. Distributed estimation of algebraic connectivity of directed networks
Systems & Control Letters 6 (03) 57 54 Contents lists available at SciVerse ScienceDirect Systems & Control Letters journal homepage: wwwelseviercom/locate/sysconle Distributed estimation of algebraic
More informationNCS Lecture 8 A Primer on Graph Theory. Cooperative Control Applications
NCS Lecture 8 A Primer on Graph Theory Richard M. Murray Control and Dynamical Systems California Institute of Technology Goals Introduce some motivating cooperative control problems Describe basic concepts
More informationDistributed Coordinated Tracking With Reduced Interaction via a Variable Structure Approach Yongcan Cao, Member, IEEE, and Wei Ren, Member, IEEE
IEEE TRANSACTIONS ON AUTOMATIC CONTROL, VOL. 57, NO. 1, JANUARY 2012 33 Distributed Coordinated Tracking With Reduced Interaction via a Variable Structure Approach Yongcan Cao, Member, IEEE, and Wei Ren,
More informationOn the simultaneous diagonal stability of a pair of positive linear systems
On the simultaneous diagonal stability of a pair of positive linear systems Oliver Mason Hamilton Institute NUI Maynooth Ireland Robert Shorten Hamilton Institute NUI Maynooth Ireland Abstract In this
More informationObtaining Consensus of Multi-agent Linear Dynamic Systems
Obtaining Consensus of Multi-agent Linear Dynamic Systems M I GRCÍ-PLNS Universitat Politècnica de Catalunya Departament de Matemàtica plicada Mineria 1, 08038 arcelona SPIN mariaisabelgarcia@upcedu bstract:
More information1520 IEEE TRANSACTIONS ON AUTOMATIC CONTROL, VOL. 49, NO. 9, SEPTEMBER Reza Olfati-Saber, Member, IEEE, and Richard M. Murray, Member, IEEE
1520 IEEE TRANSACTIONS ON AUTOMATIC CONTROL, VOL. 49, NO. 9, SEPTEMBER 2004 Consensus Problems in Networks of Agents With Switching Topology and Time-Delays Reza Olfati-Saber, Member, IEEE, and Richard
More informationAverage-Consensus of Multi-Agent Systems with Direct Topology Based on Event-Triggered Control
Outline Background Preliminaries Consensus Numerical simulations Conclusions Average-Consensus of Multi-Agent Systems with Direct Topology Based on Event-Triggered Control Email: lzhx@nankai.edu.cn, chenzq@nankai.edu.cn
More informationOn Quantized Consensus by Means of Gossip Algorithm Part I: Convergence Proof
Submitted, 9 American Control Conference (ACC) http://www.cds.caltech.edu/~murray/papers/8q_lm9a-acc.html On Quantized Consensus by Means of Gossip Algorithm Part I: Convergence Proof Javad Lavaei and
More informationDistributed Randomized Algorithms for the PageRank Computation Hideaki Ishii, Member, IEEE, and Roberto Tempo, Fellow, IEEE
IEEE TRANSACTIONS ON AUTOMATIC CONTROL, VOL. 55, NO. 9, SEPTEMBER 2010 1987 Distributed Randomized Algorithms for the PageRank Computation Hideaki Ishii, Member, IEEE, and Roberto Tempo, Fellow, IEEE Abstract
More informationFinite-Time Distributed Consensus in Graphs with Time-Invariant Topologies
Finite-Time Distributed Consensus in Graphs with Time-Invariant Topologies Shreyas Sundaram and Christoforos N Hadjicostis Abstract We present a method for achieving consensus in distributed systems in
More informationMinimum Repair Bandwidth for Exact Regeneration in Distributed Storage
1 Minimum Repair andwidth for Exact Regeneration in Distributed Storage Vivec R Cadambe, Syed A Jafar, Hamed Malei Electrical Engineering and Computer Science University of California Irvine, Irvine, California,
More informationMulti-Robotic Systems
CHAPTER 9 Multi-Robotic Systems The topic of multi-robotic systems is quite popular now. It is believed that such systems can have the following benefits: Improved performance ( winning by numbers ) Distributed
More informationComplex Laplacians and Applications in Multi-Agent Systems
1 Complex Laplacians and Applications in Multi-Agent Systems Jiu-Gang Dong, and Li Qiu, Fellow, IEEE arxiv:1406.186v [math.oc] 14 Apr 015 Abstract Complex-valued Laplacians have been shown to be powerful
More informationScaling the Size of a Multiagent Formation via Distributed Feedback
Scaling the Size of a Multiagent Formation via Distributed Feedback Samuel Coogan, Murat Arcak, Magnus Egerstedt Abstract We consider a multiagent coordination problem where the objective is to steer a
More informationTracking control for multi-agent consensus with an active leader and variable topology
Automatica 42 (2006) 1177 1182 wwwelseviercom/locate/automatica Brief paper Tracking control for multi-agent consensus with an active leader and variable topology Yiguang Hong a,, Jiangping Hu a, Linxin
More informationLecture 8 : Eigenvalues and Eigenvectors
CPS290: Algorithmic Foundations of Data Science February 24, 2017 Lecture 8 : Eigenvalues and Eigenvectors Lecturer: Kamesh Munagala Scribe: Kamesh Munagala Hermitian Matrices It is simpler to begin with
More informationOUTPUT CONSENSUS OF HETEROGENEOUS LINEAR MULTI-AGENT SYSTEMS BY EVENT-TRIGGERED CONTROL
OUTPUT CONSENSUS OF HETEROGENEOUS LINEAR MULTI-AGENT SYSTEMS BY EVENT-TRIGGERED CONTROL Gang FENG Department of Mechanical and Biomedical Engineering City University of Hong Kong July 25, 2014 Department
More informationZeno-free, distributed event-triggered communication and control for multi-agent average consensus
Zeno-free, distributed event-triggered communication and control for multi-agent average consensus Cameron Nowzari Jorge Cortés Abstract This paper studies a distributed event-triggered communication and
More informationZ-Pencils. November 20, Abstract
Z-Pencils J. J. McDonald D. D. Olesky H. Schneider M. J. Tsatsomeros P. van den Driessche November 20, 2006 Abstract The matrix pencil (A, B) = {tb A t C} is considered under the assumptions that A is
More informationObservability and Controllability Verification in Multi-Agent Systems through Decentralized Laplacian Spectrum Estimation
1 Observability and Controllability Verification in Multi-Agent Systems through Decentralized Laplacian Spectrum Estimation Mauro Franceschelli, Simone Martini, Magnus Egerstedt, Antonio Bicchi, Alessandro
More informationON THE QR ITERATIONS OF REAL MATRICES
Unspecified Journal Volume, Number, Pages S????-????(XX- ON THE QR ITERATIONS OF REAL MATRICES HUAJUN HUANG AND TIN-YAU TAM Abstract. We answer a question of D. Serre on the QR iterations of a real matrix
More informationStability Analysis of Stochastically Varying Formations of Dynamic Agents
Stability Analysis of Stochastically Varying Formations of Dynamic Agents Vijay Gupta, Babak Hassibi and Richard M. Murray Division of Engineering and Applied Science California Institute of Technology
More informationj=1 u 1jv 1j. 1/ 2 Lemma 1. An orthogonal set of vectors must be linearly independent.
Lecture Notes: Orthogonal and Symmetric Matrices Yufei Tao Department of Computer Science and Engineering Chinese University of Hong Kong taoyf@cse.cuhk.edu.hk Orthogonal Matrix Definition. Let u = [u
More informationOn Eigenvalues of Laplacian Matrix for a Class of Directed Signed Graphs
On Eigenvalues of Laplacian Matrix for a Class of Directed Signed Graphs Saeed Ahmadizadeh a, Iman Shames a, Samuel Martin b, Dragan Nešić a a Department of Electrical and Electronic Engineering, Melbourne
More informationTHE SPECTRUM OF THE LAPLACIAN MATRIX OF A BALANCED 2 p -ARY TREE
Proyecciones Vol 3, N o, pp 131-149, August 004 Universidad Católica del Norte Antofagasta - Chile THE SPECTRUM OF THE LAPLACIAN MATRIX OF A BALANCED p -ARY TREE OSCAR ROJO Universidad Católica del Norte,
More informationMarkov Chains, Random Walks on Graphs, and the Laplacian
Markov Chains, Random Walks on Graphs, and the Laplacian CMPSCI 791BB: Advanced ML Sridhar Mahadevan Random Walks! There is significant interest in the problem of random walks! Markov chain analysis! Computer
More informationTowards Decentralization of Multi-robot Navigation Functions
Towards Decentralization of Multi-robot Navigation Functions Herbert G Tanner and Amit Kumar Mechanical Engineering Dept University of New Mexico Abstract We present a navigation function through which
More informationDecentralized Stabilization of Heterogeneous Linear Multi-Agent Systems
1 Decentralized Stabilization of Heterogeneous Linear Multi-Agent Systems Mauro Franceschelli, Andrea Gasparri, Alessandro Giua, and Giovanni Ulivi Abstract In this paper the formation stabilization problem
More informationLecture 4: Introduction to Graph Theory and Consensus. Cooperative Control Applications
Lecture 4: Introduction to Graph Theory and Consensus Richard M. Murray Caltech Control and Dynamical Systems 16 March 2009 Goals Introduce some motivating cooperative control problems Describe basic concepts
More informationOn Distributed Coordination of Mobile Agents with Changing Nearest Neighbors
On Distributed Coordination of Mobile Agents with Changing Nearest Neighbors Ali Jadbabaie Department of Electrical and Systems Engineering University of Pennsylvania Philadelphia, PA 19104 jadbabai@seas.upenn.edu
More informationActive Passive Networked Multiagent Systems
Active Passive Networked Multiagent Systems Tansel Yucelen and John Daniel Peterson Abstract This paper introduces an active passive networked multiagent system framework, which consists of agents subject
More information22.3. Repeated Eigenvalues and Symmetric Matrices. Introduction. Prerequisites. Learning Outcomes
Repeated Eigenvalues and Symmetric Matrices. Introduction In this Section we further develop the theory of eigenvalues and eigenvectors in two distinct directions. Firstly we look at matrices where one
More informationObservability and Controllability Verification in Multi-Agent Systems through Decentralized Laplacian Spectrum Estimation
Observability and Controllability Verification in Multi-Agent Systems through Decentralized Laplacian Spectrum Estimation Mauro Franceschelli, Simone Martini, Magnus Egerstedt, Antonio Bicchi, Alessandro
More informationMa/CS 6b Class 23: Eigenvalues in Regular Graphs
Ma/CS 6b Class 3: Eigenvalues in Regular Graphs By Adam Sheffer Recall: The Spectrum of a Graph Consider a graph G = V, E and let A be the adjacency matrix of G. The eigenvalues of G are the eigenvalues
More informationAutomatica. Distributed discrete-time coordinated tracking with a time-varying reference state and limited communication
Automatica 45 (2009 1299 1305 Contents lists available at ScienceDirect Automatica journal homepage: www.elsevier.com/locate/automatica Brief paper Distributed discrete-time coordinated tracking with a
More informationA Distributed Newton Method for Network Utility Maximization, I: Algorithm
A Distributed Newton Method for Networ Utility Maximization, I: Algorithm Ermin Wei, Asuman Ozdaglar, and Ali Jadbabaie October 31, 2012 Abstract Most existing wors use dual decomposition and first-order
More informationDistributed Finite-Time Computation of Digraph Parameters: Left-Eigenvector, Out-Degree and Spectrum
Distributed Finite-Time Computation of Digraph Parameters: Left-Eigenvector, Out-Degree and Spectrum Themistoklis Charalambous, Member, IEEE, Michael G. Rabbat, Member, IEEE, Mikael Johansson, Member,
More informationConsensus of Information Under Dynamically Changing Interaction Topologies
Consensus of Information Under Dynamically Changing Interaction Topologies Wei Ren and Randal W. Beard Abstract This paper considers the problem of information consensus among multiple agents in the presence
More informationConsensus Seeking in Multi-agent Systems Under Dynamically Changing Interaction Topologies
IEEE TRANSACTIONS ON AUTOMATIC CONTROL, SUBMITTED FOR PUBLICATION AS A TECHNICAL NOTE. 1 Consensus Seeking in Multi-agent Systems Under Dynamically Changing Interaction Topologies Wei Ren, Student Member,
More informationExact Consensus Controllability of Multi-agent Linear Systems
Exact Consensus Controllability of Multi-agent Linear Systems M. ISAEL GARCÍA-PLANAS Universitat Politècnica de Catalunya Departament de Matèmatiques Minería 1, Esc. C, 1-3, 08038 arcelona SPAIN maria.isabel.garcia@upc.edu
More informationQuantized Average Consensus on Gossip Digraphs
Quantized Average Consensus on Gossip Digraphs Hideaki Ishii Tokyo Institute of Technology Joint work with Kai Cai Workshop on Uncertain Dynamical Systems Udine, Italy August 25th, 2011 Multi-Agent Consensus
More informationAN ITERATIVE METHOD WITH ERROR ESTIMATORS
AN ITERATIVE METHOD WITH ERROR ESTIMATORS D. CALVETTI, S. MORIGI, L. REICHEL, AND F. SGALLARI Abstract. Iterative methods for the solution of linear systems of equations produce a sequence of approximate
More informationOn the reachability and observability. of path and cycle graphs
On the reachability and observability 1 of path and cycle graphs Gianfranco Parlangeli Giuseppe Notarstefano arxiv:1109.3556v1 [math.oc] 16 Sep 2011 Abstract In this paper we investigate the reachability
More informationThe Second Eigenvalue of the Google Matrix
The Second Eigenvalue of the Google Matrix Taher H. Haveliwala and Sepandar D. Kamvar Stanford University {taherh,sdkamvar}@cs.stanford.edu Abstract. We determine analytically the modulus of the second
More informationConsensus Tracking for Multi-Agent Systems with Nonlinear Dynamics under Fixed Communication Topologies
Proceedings of the World Congress on Engineering and Computer Science Vol I WCECS, October 9-,, San Francisco, USA Consensus Tracking for Multi-Agent Systems with Nonlinear Dynamics under Fixed Communication
More informationQuestion: Given an n x n matrix A, how do we find its eigenvalues? Idea: Suppose c is an eigenvalue of A, then what is the determinant of A-cI?
Section 5. The Characteristic Polynomial Question: Given an n x n matrix A, how do we find its eigenvalues? Idea: Suppose c is an eigenvalue of A, then what is the determinant of A-cI? Property The eigenvalues
More informationMULTI-AGENT TRACKING OF A HIGH-DIMENSIONAL ACTIVE LEADER WITH SWITCHING TOPOLOGY
Jrl Syst Sci & Complexity (2009) 22: 722 731 MULTI-AGENT TRACKING OF A HIGH-DIMENSIONAL ACTIVE LEADER WITH SWITCHING TOPOLOGY Yiguang HONG Xiaoli WANG Received: 11 May 2009 / Revised: 16 June 2009 c 2009
More informationConsensus Problems in Networks of Agents with Switching Topology and Time-Delays
Consensus Problems in Networks of Agents with Switching Topology and Time-Delays Reza Olfati Saber Richard M. Murray Control and Dynamical Systems California Institute of Technology e-mails: {olfati,murray}@cds.caltech.edu
More informationChapter 3 Transformations
Chapter 3 Transformations An Introduction to Optimization Spring, 2014 Wei-Ta Chu 1 Linear Transformations A function is called a linear transformation if 1. for every and 2. for every If we fix the bases
More informationGraph Theoretic Methods in the Stability of Vehicle Formations
Graph Theoretic Methods in the Stability of Vehicle Formations G. Lafferriere, J. Caughman, A. Williams gerardol@pd.edu, caughman@pd.edu, ancaw@pd.edu Abstract This paper investigates the stabilization
More informationAgreement Problems in Networks with Directed Graphs and Switching Topology
Technical Report CIT-CDS 3 5 Agreement Problems in Networks with Directed Graphs and Switching Topology Reza Olfati Saber Richard M. Murray Control and Dynamical Systems California Institute of Technology
More informationRepeated Eigenvalues and Symmetric Matrices
Repeated Eigenvalues and Symmetric Matrices. Introduction In this Section we further develop the theory of eigenvalues and eigenvectors in two distinct directions. Firstly we look at matrices where one
More informationAsymmetric Randomized Gossip Algorithms for Consensus
Asymmetric Randomized Gossip Algorithms for Consensus F. Fagnani S. Zampieri Dipartimento di Matematica, Politecnico di Torino, C.so Duca degli Abruzzi, 24, 10129 Torino, Italy, (email: fabio.fagnani@polito.it).
More informationCombining distance-based formation shape control with formation translation
Combining distance-based formation shape control with formation translation Brian D O Anderson, Zhiyun Lin and Mohammad Deghat Abstract Steepest descent control laws can be used for formation shape control
More informationDistributed Averaging with Flow Constraints
Distributed Averaging with Flow Constraints Miroslav Barić and Francesco Borrelli University of California, Berkeley Department of Mechanical Engineering Technical report Abstract A network of storage
More informationDistributed Consensus and Linear Functional Calculation in Networks: An Observability Perspective
Distributed Consensus and Linear Functional Calculation in Networks: An Observability Perspective ABSTRACT Shreyas Sundaram Coordinated Science Laboratory and Dept of Electrical and Computer Engineering
More information1 Matrix notation and preliminaries from spectral graph theory
Graph clustering (or community detection or graph partitioning) is one of the most studied problems in network analysis. One reason for this is that there are a variety of ways to define a cluster or community.
More informationConsensus seeking on moving neighborhood model of random sector graphs
Consensus seeking on moving neighborhood model of random sector graphs Mitra Ganguly School of Physical Sciences, Jawaharlal Nehru University, New Delhi, India Email: gangulyma@rediffmail.com Timothy Eller
More informationEIGENVALUES AND SINGULAR VALUE DECOMPOSITION
APPENDIX B EIGENVALUES AND SINGULAR VALUE DECOMPOSITION B.1 LINEAR EQUATIONS AND INVERSES Problems of linear estimation can be written in terms of a linear matrix equation whose solution provides the required
More informationON THE QUALITY OF SPECTRAL SEPARATORS
ON THE QUALITY OF SPECTRAL SEPARATORS STEPHEN GUATTERY AND GARY L. MILLER Abstract. Computing graph separators is an important step in many graph algorithms. A popular technique for finding separators
More informationFormation Control and Network Localization via Distributed Global Orientation Estimation in 3-D
Formation Control and Network Localization via Distributed Global Orientation Estimation in 3-D Byung-Hun Lee and Hyo-Sung Ahn arxiv:1783591v1 [cssy] 1 Aug 17 Abstract In this paper, we propose a novel
More informationLinear algebra and applications to graphs Part 1
Linear algebra and applications to graphs Part 1 Written up by Mikhail Belkin and Moon Duchin Instructor: Laszlo Babai June 17, 2001 1 Basic Linear Algebra Exercise 1.1 Let V and W be linear subspaces
More informationIterative Methods for Solving A x = b
Iterative Methods for Solving A x = b A good (free) online source for iterative methods for solving A x = b is given in the description of a set of iterative solvers called templates found at netlib: http
More informationNewton-like method with diagonal correction for distributed optimization
Newton-lie method with diagonal correction for distributed optimization Dragana Bajović Dušan Jaovetić Nataša Krejić Nataša Krlec Jerinić August 15, 2015 Abstract We consider distributed optimization problems
More informationDecentralized control of complex systems: an overview
Decentralized control of complex systems: an overview Madhu N. Belur (Course taught with Debraj Chakraborty) Control and Computing group Department of Electrical Engineering Indian Institute of Technology
More informationLinear Algebra: Matrix Eigenvalue Problems
CHAPTER8 Linear Algebra: Matrix Eigenvalue Problems Chapter 8 p1 A matrix eigenvalue problem considers the vector equation (1) Ax = λx. 8.0 Linear Algebra: Matrix Eigenvalue Problems Here A is a given
More informationStructural Consensus Controllability of Singular Multi-agent Linear Dynamic Systems
Structural Consensus Controllability of Singular Multi-agent Linear Dynamic Systems M. ISAL GARCÍA-PLANAS Universitat Politècnica de Catalunya Departament de Matèmatiques Minería 1, sc. C, 1-3, 08038 arcelona
More informationA = 3 B = A 1 1 matrix is the same as a number or scalar, 3 = [3].
Appendix : A Very Brief Linear ALgebra Review Introduction Linear Algebra, also known as matrix theory, is an important element of all branches of mathematics Very often in this course we study the shapes
More informationRemark 1 By definition, an eigenvector must be a nonzero vector, but eigenvalue could be zero.
Sec 5 Eigenvectors and Eigenvalues In this chapter, vector means column vector Definition An eigenvector of an n n matrix A is a nonzero vector x such that A x λ x for some scalar λ A scalar λ is called
More informationBearing Rigidity and Almost Global Bearing-Only Formation Stabilization
1 Bearing Rigidity and Almost Global Bearing-Only Formation Stabilization Shiyu Zhao and Daniel Zelazo arxiv:1408.6552v4 [cs.sy] 8 Jul 2015 Abstract A fundamental problem that the bearing rigidity theory
More informationLast Time. Social Network Graphs Betweenness. Graph Laplacian. Girvan-Newman Algorithm. Spectral Bisection
Eigenvalue Problems Last Time Social Network Graphs Betweenness Girvan-Newman Algorithm Graph Laplacian Spectral Bisection λ 2, w 2 Today Small deviation into eigenvalue problems Formulation Standard eigenvalue
More informationConsensus Seeking in Multi-agent Systems Under Dynamically Changing Interaction Topologies
IEEE TRANSACTIONS ON AUTOMATIC CONTROL, SUBMITTED FOR PUBLICATION AS A TECHNICAL NOTE. Consensus Seeking in Multi-agent Systems Under Dynamically Changing Interaction Topologies Wei Ren, Student Member,
More informationDistributed Structural Stabilization and Tracking for Formations of Dynamic Multi-Agents
CDC02-REG0736 Distributed Structural Stabilization and Tracking for Formations of Dynamic Multi-Agents Reza Olfati-Saber Richard M Murray California Institute of Technology Control and Dynamical Systems
More informationTHERE is an increasing interest in multi-robot systems.
1 Distributed Consensus on Robot Networs for Dynamically Merging Feature-Based Maps Rosario Aragues, Student Member, IEEE, Jorge Cortes, Senior Member, IEEE, Carlos Sagues, Senior Member, IEEE Abstract
More informationDecentralized Formation of Random Regular Graphs for Robust Multi-Agent Networks
Decentralized Formation of Random Regular Graphs for Robust Multi-Agent Networks A. Yasin Yazıcıoğlu, Magnus Egerstedt, and Jeff S. Shamma Abstract Multi-agent networks are often modeled via interaction
More informationLinear Algebra Massoud Malek
CSUEB Linear Algebra Massoud Malek Inner Product and Normed Space In all that follows, the n n identity matrix is denoted by I n, the n n zero matrix by Z n, and the zero vector by θ n An inner product
More informationLecture 1 and 2: Introduction and Graph theory basics. Spring EE 194, Networked estimation and control (Prof. Khan) January 23, 2012
Lecture 1 and 2: Introduction and Graph theory basics Spring 2012 - EE 194, Networked estimation and control (Prof. Khan) January 23, 2012 Spring 2012: EE-194-02 Networked estimation and control Schedule
More informationConceptual Questions for Review
Conceptual Questions for Review Chapter 1 1.1 Which vectors are linear combinations of v = (3, 1) and w = (4, 3)? 1.2 Compare the dot product of v = (3, 1) and w = (4, 3) to the product of their lengths.
More informationFormation Control of Nonholonomic Mobile Robots
Proceedings of the 6 American Control Conference Minneapolis, Minnesota, USA, June -6, 6 FrC Formation Control of Nonholonomic Mobile Robots WJ Dong, Yi Guo, and JA Farrell Abstract In this paper, formation
More informationMobile Robotics 1. A Compact Course on Linear Algebra. Giorgio Grisetti
Mobile Robotics 1 A Compact Course on Linear Algebra Giorgio Grisetti SA-1 Vectors Arrays of numbers They represent a point in a n dimensional space 2 Vectors: Scalar Product Scalar-Vector Product Changes
More informationWeighted Gossip: Distributed Averaging Using Non-Doubly Stochastic Matrices
Weighted Gossip: Distributed Averaging Using Non-Doubly Stochastic Matrices Florence Bénézit ENS-INRIA, France Vincent Blondel UCL, Belgium Patric Thiran EPFL, Switzerland John Tsitsilis MIT, USA Martin
More informationLecture 7. Econ August 18
Lecture 7 Econ 2001 2015 August 18 Lecture 7 Outline First, the theorem of the maximum, an amazing result about continuity in optimization problems. Then, we start linear algebra, mostly looking at familiar
More informationEigenvalues and Eigenvectors
5 Eigenvalues and Eigenvectors 5.2 THE CHARACTERISTIC EQUATION DETERMINANATS n n Let A be an matrix, let U be any echelon form obtained from A by row replacements and row interchanges (without scaling),
More informationLaplacian Integral Graphs with Maximum Degree 3
Laplacian Integral Graphs with Maximum Degree Steve Kirkland Department of Mathematics and Statistics University of Regina Regina, Saskatchewan, Canada S4S 0A kirkland@math.uregina.ca Submitted: Nov 5,
More information1 Matrix notation and preliminaries from spectral graph theory
Graph clustering (or community detection or graph partitioning) is one of the most studied problems in network analysis. One reason for this is that there are a variety of ways to define a cluster or community.
More informationRemark By definition, an eigenvector must be a nonzero vector, but eigenvalue could be zero.
Sec 6 Eigenvalues and Eigenvectors Definition An eigenvector of an n n matrix A is a nonzero vector x such that A x λ x for some scalar λ A scalar λ is called an eigenvalue of A if there is a nontrivial
More informationA Distributed Newton Method for Network Optimization
A Distributed Newton Method for Networ Optimization Ali Jadbabaie, Asuman Ozdaglar, and Michael Zargham Abstract Most existing wor uses dual decomposition and subgradient methods to solve networ optimization
More informationConjugate Gradient (CG) Method
Conjugate Gradient (CG) Method by K. Ozawa 1 Introduction In the series of this lecture, I will introduce the conjugate gradient method, which solves efficiently large scale sparse linear simultaneous
More information