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  • 1. 1Algorithms for Leader Selection inStochastically Forced Consensus NetworksFu Lin, Makan Fardad, and Mihailo R. Jovanovi´cAbstractWe examine the leader selection problem in stochastically forced consensus networks. A node is aleader if, in addition to relative information from its neighbors, it also has an access to its own state.This problem arises in several applications including control of vehicular formations and localizationin sensor networks. We are interested in selecting an a priori specified number of leaders such that thesteady-state variance of the deviation from consensus is minimized. Even though we establish convexityof the objective function, combinatorial nature of constraints makes determination of the global minimumdifficult for large networks. We introduce a convex relaxation of constraints to obtain a lower boundon the global optimal value. We also use a simple but efficient greedy algorithm and the alternatingdirection method of multipliers to compute upper bounds. Furthermore, for networks with noise-freeleaders that perfectly follow their desired trajectories, a sequence of convex relaxations is used to identifythe leaders. Several examples ranging from regular lattices to random graphs are provided to illustratethe effectiveness of the developed algorithms.Index TermsAlternating direction method of multipliers, consensus networks, convex optimization, convex re-laxations, greedy algorithm, leader selection, performance bounds, semidefinite programming, sensorselection, sparsity, variance amplification.I. INTRODUCTIONReaching consensus in a decentralized fashion is an important problem in network science [1].This problem is often encountered in social networks where a group of individuals is trying toagree on a certain issue [2], [3]. A related problem has been studied extensively in computerFinancial support from the National Science Foundation under CAREER Award CMMI-06-44793 and under awards CMMI-09-27720 and CMMI-0927509 is gratefully acknowledged.F. Lin and M. R. Jovanovi´c are with the Department of Electrical and Computer Engineering, University of Minnesota,Minneapolis, MN 55455. M. Fardad is with the Department of Electrical Engineering and Computer Science, Syracuse University,NY 13244. E-mails:,, 12, 2012 DRAFT
  • 2. 2science with the objective of distributing evenly computational load over a network of proces-sors [4], [5]. Recently, consensus problem has received considerable attention in the contextof distributed control [6]–[9]. For example, in cooperative control of vehicular formations, itis desired to use local interactions between vehicles in order to reach agreement on quantitiessuch as heading angle, velocity, and inter-vehicular spacing. Since vehicles have to maintainagreement in the presence of uncertainty it is of importance to study robustness of consensus.Several authors have recently used the steady-state variance of the deviation from consensus tocharacterize performance limitations of stochastically forced networks [10]–[16].In this paper, we consider undirected consensus networks with two groups of nodes. Ordinarynodes, so-called followers, form their action using relative information exchange with theirneighbors; special nodes, so-called leaders, also have access to their own states. This settingmay arise in the control of vehicular formations where all vehicles are equipped with rangingdevices (that provide information about relative distances with respect to their neighbors), andthe leaders additionally have GPS devices (that provide information with respect to a globalframe of reference).We are interested in assigning an a priori specified number of nodes as leaders in order tominimize the steady-state variance of the deviation from consensus. For undirected networks inwhich all nodes are subject to stochastic disturbances, we establish convexity of the objectivefunction. In spite of this, combinatorial nature of Boolean constraints (a node is either a leaderor it is not) makes determination of the global minimum challenging for large networks. Instead,we focus on computing lower and upper bounds on the global optimal value. Convex relaxationof Boolean constraints is used to obtain a lower bound, and two different algorithms are used toobtain an upper bound and to identify leaders. The first algorithm utilizes one-leader-at-a-time(greedy) approach followed by a swap procedure that improves performance by checking possibleswaps between leaders and followers. In both steps, algorithmic complexity is significantlyreduced by exploiting structure of low-rank modifications to Laplacian matrices. The secondalgorithm utilizes the alternating direction method of multipliers (ADMM) [17] which is capableof handling the nonconvex Boolean constraints by a simple projection. Computational efficiencyof these algorithms makes them well-suited for establishing achievable performance bounds forleader selection problem in large stochastically forced networks.Following [18]–[21], we also examine consensus networks in which leaders follow desiredOctober 12, 2012 DRAFT
  • 3. 3trajectories at all times. For this idealized case, the identification of noise-free leaders is addition-ally complicated by nonconvexity of the objective function. For consensus networks with at leastone leader, adding leaders always improves performance [18]. In view of this, a greedy algorithmthat selects one leader at a time by assigning the node that leads to the largest performance im-provement as a leader was proposed in [18]. Furthermore, it was proved in [20] that the varianceof deviation from consensus is a supermodular function of the set of noise-free leaders. Thus, thesupermodular optimization framework in conjunction with the greedy algorithm can be used toprovide selection of leaders that is within a provable bound from globally optimal solution [20].In contrast to the above references, we use convex optimization to select noise-free leaders.An alternative explicit expression for the objective function that we provide is used to identifythe source of nonconvexity and to suggest an LMI-based convex relaxation. In addition to this,we relax the hard Boolean-valued constraint on the number of leaders with a soft one. This isachieved by augmenting the objective function with the sparsity-promoting term that penalizesthe 1 norm of the vector of optimization variables [22], [23]. The 1 norm provides a means forobtaining a sparse solution whose nonzero elements identify the leaders. The developed algorithmproduces a tradeoff curve between the number of noise-free leaders and the variance of thedeviation from consensus by solving a parameterized family of convex optimization problems.The controllability of leader-follower consensus networks is also an active area of research [24]–[27]. Recent efforts have focused on characterizing graph-theoretic conditions for controllabilityof the network in which a number of pre-specified leaders act as control inputs. In contrast,the leader selection problem aims at identifying leaders that are most effective in maintainingconsensus in the presence of disturbances. Other related work on augmenting topologies ofnetworks to improve algebraic connectivity includes [28]–[30].The paper is organized as follows. In Section II, we formulate the leader selection problemand establish connections with the sensor selection problem. In Section III, we propose an LMI-based convex relaxation of the objective function in the noise-free leader selection problem.Furthermore, instead of imposing Boolean constraints, we augment the objective function with the1 norm of the optimization variable. In Section IV, we develop efficient algorithms to computelower and upper bounds on the global optimal value for the noise-corrupted leader selectionproblem. Finally, we conclude the paper with a summary of our contributions in Section V.October 12, 2012 DRAFT
  • 4. 4II. PROBLEM FORMULATIONIn this section, we formulate the noise-corrupted and noise-free leader selection problemsin consensus networks and make connections to sensor selection problem in sensor networks.Furthermore, we establish an equivalence between the noise-corrupted and noise-free leaderselection problems when all leaders use arbitrarily large feedback gains on their own states.A. Leader selection problem in consensus networksWe consider n single-integrators˙ψi = ui + wi, i = 1, . . . , nwhere ψi is the scalar state, ui is the control input, and wi is the white stochastic disturbance withzero-mean and unit-variance. A node is a follower if it uses only relative information exchangewith its neighbors to form its control action,ui = −j ∈ Ni(ψi − ψj).A node is a leader if, in addition to relative information exchange with its neighbors, it also hasaccess to its own stateui = −j ∈ Ni(ψi − ψj) − κi ψi.Here, κi is a positive number and Ni is the set of all nodes that node i communicates with.The communication network is modeled by a connected, undirected graph; thus, the graphLaplacian L is a symmetric positive semidefinite matrix with a single eigenvalue at zero andthe corresponding eigenvector 1 of all ones. A state-space representation of the leader-followerconsensus network is given by˙ψ = − (L + DκDx) ψ + w (1)whereDκ := diag (κ) , Dx := diag (x)are diagonal matrices formed from the vectors κ = [ κ1 · · · κn ]Tand x = [ x1 · · · xn ]T. Here,x is a Boolean-valued vector with its ith entry xi ∈ {0, 1}, indicating that node i is a leader ifOctober 12, 2012 DRAFT
  • 5. 5xi = 1 and that node i is a follower if xi = 0. In connected networks with at least one leaderL + DκDx is a positive definite matrix and the steady-state covariance of ψ is determined byΣ := limt → ∞E ψ(t) ψT(t)=∞0e−(L+DκDx)te−(L+DκDx)T tdt=12(L + DκDx)−1.Following [12], [15], we use the total steady-state variancetrace (Σ) =12trace (L + DκDx)−1(2)to quantify performance of consensus networks subject to stochastic disturbances.We are interested in identifying Nl leaders that are most effective in reducing the steady-statevariance (2). For an a priori specified number of leaders Nl < n, the leader selection problemcan thus be formulated asminimizexJ(x) = trace ((L + DκDx)−1)subject to xi ∈ {0, 1}, i = 1, . . . , n1Tx = Nl.(LS1)In (LS1), the number of leaders Nl as well as the matrices L and Dκ are the problem data, andthe vector x is the optimization variable. As shown in Section IV, for a positive definite matrixL + DκDx, the objective function J in (LS1) is a convex function of x. The challenging aspectof (LS1) comes from the nonconvex Boolean constraints xi ∈ {0, 1}; in general, finding thesolution to (LS1) requires an intractable combinatorial search.Since the leaders are subject to stochastic disturbances, we refer to (LS1) as the noise-corruptedleader selection problem. We also consider the selection of noise-free leaders which follow theirdesired trajectories at all times [18]. Equivalently, in coordinates that determine deviation fromthe desired trajectory, the state of every leader is identically equal to zero, thereby yielding onlythe dynamics of the followers˙ψf = − Lf ψf + wf .Here, Lf is obtained from L by eliminating all rows and columns associated with the leaders.October 12, 2012 DRAFT
  • 6. 6Thus, the problem of selecting leaders that minimize the steady-state variance of ψf amounts tominimizexJf (x) = trace (L−1f )subject to xi ∈ {0, 1}, i = 1, . . . , n1Tx = Nl.(LS2)As in (LS1), the Boolean constraints xi ∈ {0, 1} are nonconvex. Furthermore, as we demonstratein Section III, the objective function Jf in (LS2) is a nonconvex function of x.In what follows, we establish equivalence between the noise-corrupted and noise-free leaderselection problems (LS1) and (LS2) when all leaders use arbitrarily large feedback gains ontheir own states. Partitioning ψ into the state of the leader nodes ψl and the state of the followernodes ψf brings system (1) to the following form1˙ψl˙ψf = −Ll + DκlL0LT0 Lfψlψf +wlwf . (3)Here, Dκl:= diag (κl) with κl ∈ RNl being the vector of feedback gains associated with theleaders. Taking the trace of the inverse of the 2 × 2 block matrix in (3) yieldsJ = trace L−1f + L−1f LT0 S−1κlL0 L−1f + S−1κlwhereSκl= Ll + Dκl− L0 L−1f LT0is the Schur complement of Lf . Since S−1κlvanishes as each component of the vector κl goesto infinity, the variance of the network is solely determined by the variance of the followers,Jf = trace L−1f , where Lf is the reduced Laplacian matrix obtained by removing all columnsand rows corresponding to the leaders from L.1Note that Dx does not show in (3) since the partition is performed with respect to the indices of the 0 and 1 diagonalelements of Dx.October 12, 2012 DRAFT
  • 7. 7B. Connections to the sensor selection problemThe problem of estimating a vector ψ ∈ Rnfrom m relative measurements corrupted byadditive white noiseyij = ψi − ψj + wijarises in distributed localization in sensor networks. We consider the simplest scenario in whichall ψi’s are scalar-valued, with ψi denoting the position of sensor i; see [10], [11] for vector-valued localization problems. Let Ir denote the index set of the m pairs of distinct nodes betweenwhich the relative measurements are taken and let eij belong to Rnwith 1 and −1 at its ith andjth elements, respectively, and zero everywhere else. Then,yij = eTij ψ + wij, (i, j) ∈ Iror, equivalently in the matrix form,yr = ETr ψ + wr (4)where yr is the vector of relative measurements and Er ∈ Rn×mis the matrix whose columnsare determined by eij for (i, j) ∈ Ir. Since ψ + a1 for any scalar a results in the same yr, withrelative measurements the position vector ψ can be determined only up to an additive constant.This can also be verified by noting that ETr 1 = 0.Suppose that Nl sensors can be equipped with GPS devices that allow them to measure theirabsolute positionsya = ETa ψ + ETa wawhere Ea ∈ Rn×Nl is the matrix whose columns are determined by ei, the ith unit vector inRn, for i ∈ Ia, the index set of absolute measurements. Then the vector of all measurements isgiven by yrya =ETrETa ψ +I 00 ETawrwa (5)where wr and wa are zero-mean white stochastic disturbances withE(wrwTr ) = Wr, E(wawTa ) = Wa, E(wrwTa ) = 0.October 12, 2012 DRAFT
  • 8. 8In Appendix A, we show that the problem of choosing Nl absolute position measurementsamong n sensors to minimize the variance of the estimation error is equivalent to the noise-corrupted leader selection problem (LS1). Furthermore, when the positions of Nl sensors areknown a priori we show that the problem of assigning Nl sensors to minimize the variance ofthe estimation error amounts to solving the noise-free leader selection problem (LS2).III. LINEAR APPROXIMATION AND SOFT CONSTRAINT METHOD: NOISE-FREE LEADERSIn this section, we provide an alternative expression for the objective function Jf in thenoise-free leader selection problem (LS2). We use this explicit expression to identify the sourceof nonconvexity and to suggest an LMI-based convex approximation. We then relax the hardconstraint of having exactly Nl leaders in (LS2) by augmenting the objective function Jf withthe 1 norm of the optimization variable x. This soft constraint approach yields a parameterizedfamily of optimization problems whose solution provides a tradeoff between the 1 norm of xand the convex approximation of the variance amplification of the network.A. Explicit expression for the objective functionSince the objective function Jf in (LS2) is not expressed explicitly in terms of the optimizationvariable x, it is difficult to examine its basic properties (including convexity). We next providean alternative expression for Jf that allows us to establish lack of convexity and to suggest anLMI-based convex approximation of Jf .Proposition 1: For networks with at least one leader, the objective function Jf in the noise-freeleader selection problem (LS2) can be written asJf = trace L−1f = trace (I − Dx)(G + Dx ◦ L)−1(I − Dx) (6)where ◦ denotes the elementwise multiplication of matrices, andG = (I − Dx) L (I − Dx), Dx = diag (x) , xi ∈ {0, 1}, i = 1, . . . , n.Furthermore, Jf is a nonconvex function of x over the smallest convex set xi ∈ [0, 1] thatcontains feasible points xi ∈ {0, 1} for i = 1, . . . , n.October 12, 2012 DRAFT
  • 9. 9Proof: After an appropriate relabeling of the nodes (as done in (3)), L and Dx can bepartitioned conformably into 2 × 2 block matrices,L =Ll L0LT0 Lf , Dx =INl×Nl0Nl×p0p×Nl0p×p , p := n − Nlwhich leads toG =0Nl×Nl0Nl×p0p×NlLf , Dx ◦ L =INl×Nl◦ Ll 0Nl×p0p×Nl0p×pG + Dx ◦ L =INl×Nl◦ Ll 0Nl×p0p×NlLf .Since INl×Nl◦Ll is a diagonal matrix with positive diagonal elements and since the principal sub-matrix Lf of the Laplacian L is positive definite for connected graphs [1, Lemma 10.36], we haveG + Dx ◦ L 0. (7)Consequently,trace (I − Dx)(G + Dx ◦ L)−1(I − Dx) = trace (L−1f )which yields the desired result (6).We next use a counterexample to illustrate the lack of convexity of Jf over xi ∈ [0, 1]. LetL =1 −1−1 1 , Dx =x1 00 x2with x1 ∈ [0, 1] and x2 = 1. FromG + L ◦ Dx =(1 − x1)2+ x1 00 1 0 and Jf =(1 − x1)2(1 − x1)2 + x1it can be verified that, for x1 ∈ [0, 1/3], the second derivative of Jf with respect to x1 is negative(implying that Jf is not convex).October 12, 2012 DRAFT
  • 10. 10Explicit expression (6) in conjunction with Schur complement can be used to convert theminimization of Jf into the following problemminimizeX, xtrace (X)subject toX I − DxI − Dx G + Dx ◦ L 0(8)where X ∈ Rn×nis a symmetric positive definite matrix. To see this, note that since G+Dx◦L0, we haveX I − DxI − Dx G + Dx ◦ L 0 ⇔ X (I − Dx)(G + Dx ◦ L)−1(I − Dx).Thus, to minimize trace (X) subject to the inequality constraint, we take X = (I − Dx)(G +Dx ◦ L)−1(I − Dx), which shows equivalence between the objective functions in (8) and in (6).Thus, the noise-free leader selection problem (LS2) can be formulated asminimizeX, xtrace (X)subject toX I − DxI − Dx G + Dx ◦ L 0G = (I − Dx) L (I − Dx)Dx = diag (x) , 1Tx = Nl, xi ∈ {0, 1}, i = 1, . . . , n.(9)In addition to the Boolean constraints, the quadratic dependence of G on Dx provides anothersource of nonconvexity in (9). Thus, in contrast to (LS1), relaxation of the Boolean constraints toxi ∈ [0, 1] for i = 1, . . . , n is not enough to guarantee convexity of the optimization problem (9).B. Linear approximation and soft constraint methodAs established in Section III-A, the alternative formulation (9) of the noise-free leader selectionproblem (LS2) identifies two sources of nonconvexity: the quadratic matrix inequality and theBoolean constraints. In view of this, we use linearization of the matrix G to approximate thequadratic matrix inequality in (9) with an LMI. Furthermore, instead of imposing Booleanconstraints, we augment the objective function with the 1 norm of x. This choice is usedas a proxy for obtaining a sparse solution x whose nonzero elements identify the leaders.October 12, 2012 DRAFT
  • 11. 11The idea of using linearization comes from [31], where a linear approximation of the objectivefunction trace (Y Z) at the point (Y0, Z0) was considered(1/2) trace (Y0Z + Y Z0).To design fixed-order output feedback controllers, the authors of [31] minimize trace (Y0Z+Y Z0)with respect to Y and Z, set Y0 ← Y , Z0 ← Z, and repeat. Motivated by this iterative scheme,we consider the following linear approximation of GG0 := (1/2) (I − Dx) L (I − Dx0 ) + (1/2) (I − Dx0 ) L (I − Dx) (10)where Dx0 is our current-best-estimate of Dx. Replacing G with G0 leads to an LMI approxi-mation of the quadratic matrix inequality in (9).In addition to the linearization, we relax the hard constraint 1Tx = Nl for Boolean-valued xwith a soft one. This is achieved by augmenting the objective function with the 1 norm of x,trace (X) + γni = 1|xi|where the positive number γ characterizes our emphasis on the sparsity of the vector x. We notethat the 1 norm x 1 is a widely used proxy for promoting sparsity [32, Chapter 6]. Puttingthis soft constraint approach and linearization (10) together, we obtain a convex optimizationproblemminimizeX, xtrace (X) + γni = 1|xi|subject toX I − DxI − Dx G0 + Dx ◦ L 0G0 = (1/2) (I − Dx) L (I − Dx0 ) + (1/2) (I − Dx0 ) L (I − Dx)Dx = diag (x)(11)which can be solved efficiently for small size problems (e.g., n ≤ 30) using standard softwaresuch as CVX [33]. For large problems, we develop a customized algorithm in Appendix B.For a fixed value of γ, we start with Dx0 = 0 and solve problem (11) as part of an iterativeloop; the solution Dx = diag (x) at every iteration is treated as the current-best-estimate Dx0 =October 12, 2012 DRAFT
  • 12. 12diag (x0) for the linearization in the next iteration until x − x0 2 ≤ . Ranging γ from smallto large values, the solution to the γ-parameterized family of problems (11) provides a tradeoffbetween minimization of trace (X) and minimization of x 1 . Larger values of γ promotesmaller x 1 and typically lead to fewer nonzero elements in x. Depending on the structure ofthe network, there may not exist values of γ that lead to a vector x with exactly Nl nonzeroelements. In this case, we find the solution x∗that has the least number of nonzero elementsN∗with N∗> Nl, and use the indices of the Nl largest entries of x∗to determine the leaders.C. Examples1) An example from [18]: We next use the soft constraint method of Section III-B to selectleaders for a small network with 25 nodes shown in Fig. 1. As shown in Figs. 2a and 2b, thenumber of leaders Nl decreases and the variance Jf of the followers increases with γ. Thetradeoff between the number of leaders and the variance of followers is illustrated in Fig. 2c.Figure 3 compares performance of the soft constraint method to performance of the greedyalgorithm [18]–[20], which chooses one leader at a time by assigning the node that provides thelargest performance improvement as a leader. Using a supermodular optimization framework, itwas shown in [20] that the greedy algorithm selects noise-free leaders that are within a provableperformance bound from the global solution to (LS2). This motivates use of greedy algorithmas a benchmark for performance of the soft constraint method. As shown in Fig. 3a, for smallnumber of leaders (e.g., Nl ≤ 5), the greedy algorithm outperforms the soft constraint method;the only exception happens for Nl = 3. A more detailed comparison is reported in Table I, withthe global solution to (LS2) for Nl ≤ 5 resulting from exhaustive search.When the number of leaders is large (e.g., Nl ≥ 9), the soft constraint method outperforms thegreedy algorithm; see Fig. 3b. The heuristics of assigning nodes with large degrees (i.e., largenumber of neighbors) as leaders is outperformed by both greedy and soft constraint methods. Thepoor performance of the simple degree-heuristics-based-selection was also noted in [18]–[20].2) A random network example: We next consider the selection of noise-free leaders in anetwork with 100 randomly distributed nodes in a unit square. A pair of nodes can communicatewith each other if their distance is not greater than 0.2. This scenario arises in sensor networkswith prescribed omnidirectional (i.e., disk shape) sensing range [1]. As shown in Figs. 4a and 4b,October 12, 2012 DRAFT
  • 13. 131241056378912111314151617 1822252324211920Fig. 1: A small network with 25 nodes [18].(a) Number of leaders Nl (b) Variance of the network Jf (c) Tradeoff between Nl and JfFig. 2: Performance of the soft constraint method for the network shown in Fig. 1: (a) the numberof leaders Nl decreases with γ; (b) the variance of the followers Jf increases with γ; and (c)the tradeoff between Nl and Jf .TABLE I: Performance comparison of greedy algorithm and soft constraint method with theglobal solution to the noise-free leader selection problem (LS2) for the network shown in Fig. solution greedy algorithm soft constraintNl Jf leaders Jf leaders Jf leaders1 66.0 13 66.0 13 112.0 252 38.4 8, 25 44.8 13, 25 64.0 16, 253 30.0 8, 16, 25 33.3 7, 13, 25 32.1 7, 16, 254 25.3 7, 9, 16, 25 27.4 7, 13, 16, 25 29.4 7, 16, 20, 255 20.7 3, 7, 9, 16, 25 22.2 3, 7, 13, 16, 25 22.6 3, 7, 16, 20, 25October 12, 2012 DRAFT
  • 14. 14(a) (b)Fig. 3: (a) The variance of the followers Jf obtained using the soft constraint method (◦),the greedy algorithm (∗), and the degree heuristics (+) for the network shown in Fig. 1. (b)Comparison of three algorithms for Nl ≥ 9.the number of leaders Nl decreases and the variance Jf of followers increases with γ; also seethe tradeoff curve between Nl and Jf in Fig. 4c.For this random network example, we observe similar selection of leaders and similar per-formance of the soft constraint and greedy algorithms. Furthermore, for Nl > 1, both thesealgorithms significantly outperform the degree-heuristics-based-selection; see Fig. 5. To gainsome insight into the selection of leaders, we compare the results obtained using soft constraintmethod and the degree heuristics. As shown in Fig. 6b, the degree heuristics chooses nodes thatturn out to be in the proximity of each other. In contrast, the soft constraint method select leadersthat, in addition to having large degrees, are far from each other; see Fig. 6a. As a result, theselected leaders can influence more followers and thus more effectively improve the performanceof the network.The contrast between degree heuristics and soft constraint method becomes even more dramaticfor large number of leaders. As shown in Figs. 6c and 6d, the leader sets obtained using thesoft constraint method and degree heuristics are almost complements of each other. While thedegree heuristics clusters the leaders around the center of the network, the soft constraint methoddistributes the leaders around the boundary of the network.Figures 7a and 7b show the degree distribution of all the nodes in the random network and ofthe 41 nodes that are selected as leaders (see Fig. 6c). In contrast to the degree heuristics, theOctober 12, 2012 DRAFT
  • 15. 15(a) Number of leaders Nl (b) Variance of the network Jf (c) Tradeoff curve between Nl and JfFig. 4: A random network with 100 nodes: (a) the number of leaders Nl decreases with γ; (b)the variance of the followers Jf increases with γ; and (c) the tradeoff curve between Nl and Jf .Fig. 5: The objective function Jf obtained using the soft constraint method (◦), the greedyalgorithm (∗), and the degree heuristics (+) for the random network.soft constraint method chooses nodes with both large- and small-degrees as leaders; in particular,all nodes with degree less than 8 and all nodes with degree greater than 18 are selected.IV. LOWER AND UPPER BOUNDS ON GLOBAL PERFORMANCE: NOISE-CORRUPTED LEADERSIn contrast to the noise-free leader selection problem (LS2), we next show that the objectivefunction in the noise-corrupted leader selection problem (LS1) is convex. We take advantageof the convexity of J in (LS1) and develop efficient algorithms to compute lower and upperbounds on the global optimal value Jopt of (LS1). A lower bound results from convex relaxationof Boolean constraints in (LS1). Furthermore, upper bounds are obtained using an efficient greedyalgorithm and the alternating direction method of multipliers (ADMM). Greedy algorithm selectsOctober 12, 2012 DRAFT
  • 16. 16(a) Nl = 5 (b) Nl = 5(c) Nl = 41 (d) Nl = 40Fig. 6: Selection of leaders (•) for the random network example using soft constraint method in(a) and (c) and using degree heuristics in (b) and (d).one leader at a time, which introduces low-rank modifications to the Laplacian matrix. We exploitthis feature in conjunction with the matrix inversion lemma to gain computational efficiency. Onthe other hand, the ADMM algorithm handles the Boolean constraints explicitly by a simpleprojection onto a discrete nonconvex set. Finally, we provide two examples to illustrate theperformance of the developed approach.A. Convex relaxation to obtain a lower boundSince the objective function J in (LS1) is the composition of a convex function trace (¯L−1)of a positive definite matrix ¯L 0 with an affine function ¯L = L + DκDx, it follows that J isa convex function of x. By enlarging the Boolean constraint set xi ∈ {0, 1} to its convex hullOctober 12, 2012 DRAFT
  • 17. 17(a) (b)Fig. 7: The degree distribution of (a) the random network of Section III-C2 and of (b) 41 leadersselected using soft constraint method. Note that the soft constraint method chooses all nodeswith degree less than 8 and all nodes with degree greater than 18.xi ∈ [0, 1] (i.e., the smallest convex set that contains the Boolean constraint set), we obtain aconvex relaxation of (LS1)minimizexJ(x) = trace (L + DκDx)−1subject to 1Tx = Nl, 0 ≤ xi ≤ 1, i = 1, . . . , n.(CR)Since we have enlarged the constraint set, the solution x∗of the relaxed problem (CR) providesa lower bound on Jopt. However, x∗may not provide a selection of Nl leaders, as it may turnout not to be Boolean-valued. If x∗is Boolean-valued, then it is the global solution of (LS1).Following similar argument as in Section III-A, Schur complement can be used to formulatethe convex optimization problem (CR) as an SDPminimizeX, xtrace (X)subject toX II L + DκDx 01Tx = Nl, 0 ≤ xi ≤ 1, i = 1, . . . , n.For small networks (e.g., n ≤ 30), this problem can be solved efficiently using standard SDPsolvers. For large networks, we develop a customized interior point method in Appendix C.October 12, 2012 DRAFT
  • 18. 18B. Greedy algorithm to obtain an upper boundWith the lower bound on the optimal value Jopt resulting from the convex relaxation (CR)in Section IV-A, we next use a greedy algorithm to compute an upper bound on Jopt. Thisalgorithm selects one leader at a time by assigning the node that provides the largest performanceimprovement as the leader. Once this is done, an attempt to improve a selection of Nl leadersis made by checking possible swaps between the leaders and the followers. Similar greedyalgorithms have been used in [18], [19] for noise-free leader selection problem. In the noise-corrupted problem, we show that substantial improvement in algorithmic complexity can beachieved by exploiting structure of the low-rank modifications to the Laplacian matrix.1) One-leader-at-a-time algorithm: As the name suggests, we select one leader at a time byassigning the node that results in the largest performance improvement as the leader. To selectthe first leader, we computeJi1 = trace (L + κieieTi )−1for i = 1, . . . , n, and assign the node, say v1, that achieves the minimum value of {Ji1}. If twoor more nodes provide the largest performance improvement, we select one of these nodes as aleader. After choosing s leaders, v1, . . . , vs, we computeJis+1 = trace (Ls + κieieTi )−1Ls = L + κv1 ev1 eTv1+ · · · + κvs evs eTvsfor i /∈ {v1, . . . , vs}, and select node vs+1 that yields the minimum value of {Jis+1}. Thisprocedure is repeated until all Nl leaders are selected.Without exploiting structure, the above procedure requires O(n4Nl) operations. On the otherhand, the rank-1 update formula obtained from matrix inversion lemma(Ls + κieieTi )−1= L−1s −L−1s κieieTi L−1s1 + κieTi L−1s ei(12)yieldsJis+1 = trace (L−1s ) −κi (L−1s )i221 + κi(L−1s )iiwhere (L−1s )i is the ith column of L−1s and (L−1s )ii is the iith entry of L−1s . To initiate theOctober 12, 2012 DRAFT
  • 19. 19algorithm, we use the generalized rank-1 update [34],L−11 = L†− (L†ei)1T− 1(L†ei)T+ ((1/κi) + eTi L†ei)11Tand thus,Ji1 = trace (L†) + n ((1/κi) + eTi L†ei)where L†denotes the pseudo-inverse of L (e.g., see [35])L†= (L + 11T/n)−1− 11T/n.Therefore, once L−1s is determined, the inverse of the matrix on the left-hand-side of (12) can becomputed using O(n2) operations and Jis+1 can be evaluated using O(n) operations. Overall, Nlrank-1 updates, nNl/2 objective function evaluations, and one full matrix inverse (for computingL−1s ) require O(n2Nl + n3) operations as opposed to O(n4Nl) operations without exploiting thelow-rank structure. In large-scale networks, further computational advantage can be gained byexploiting structure of the underlying Laplacian matrices; see [36].2) Swap algorithm: Having determined a selection of leaders using one-leader-at-a-timealgorithm, we swap one of the Nl leaders with one of the n − Nl followers, and check ifsuch a swap leads to a decrease in J. If no decrease occurs for all (n − Nl)Nl swaps, thealgorithm terminates; if a decrease in J occurs, we update the leader and then restart checkingthe possible (n − Nl)Nl swaps for the new leader selection. This swap procedure has been usedas an effective means for improving performance of combinatorial algorithms encountered ingraph partitioning [37], sensor selection [38], and community detection problems [39].Since a swap between a leader i and a follower j leads to a rank-2 modification (13) to thematrix ¯L = L + DκDx, we can exploit this low-rank structure to gain computational efficiency.Using the matrix inversion lemma, we have¯L − κieieTi + κjejeTj−1= ¯L−1− ¯L−1 ¯Eij (I2 + ETij¯L−1 ¯Eij)−1ETij¯L−1 (13)where Eij = [ ei ej ], ¯Eij = [ − κiei κjej ], and I2 is the 2 × 2 identity matrix. Thus, theobjective function after the swap between leader i and follower j is given byJij = J − trace (I2 + ETij¯L−1 ¯Eij)−1ETij¯L−2 ¯Eij . (14)October 12, 2012 DRAFT
  • 20. 20Fig. 8: A 3 × 3 grid.Here, we do not need to form the full matrix ¯L−2, sinceETij¯L−2 ¯Eij =− κi(¯L−2)ii κj(¯L−2)ij− κi(¯L−2)ji κj(¯L−2)jjand the ijth entry of ¯L−2can be computed by multiplying the ith row of ¯L−1with the jthcolumn of ¯L−1. Thus, evaluation of Jij takes O(n) operations and computation of the matrixinverse in (13) requires O(n2) operations.Remark 1: Since the total number of swaps for large-scale networks can be large, we fol-low [38] and limit the maximum number of swaps with a linear function of the number of nodesn. On the other hand, particular structure of networks can be exploited to reduce the requirednumber of swaps. To illustrate this, let us consider the problem of selecting one leader in anetwork with 9 nodes shown in Fig. 8. Suppose that nodes in set S1 := {1, 3, 7, 9} have thesame feedback gain κ1 and that nodes in set S2 := {2, 4, 6, 8} have the same feedback gain κ2.In addition, suppose that node 5 is chosen as a leader. Owing to symmetry, to check if selectingother nodes as a leader can improve performance we only need to swap node 5 with one nodein each set S1 and S2. We note that more sophisticated symmetry exploitation techniques havebeen discussed in [26], [40].C. Alternating direction method of multipliersSince the previously introduced greedy algorithm may not yield an optimal selection of leaders,we next employ the ADMM algorithm [17] as an alternative approach to a selection of Nl leadersfor problem (LS1). Although the convergence of this method depends on the initial conditions andon the algorithmic parameters, ADMM is capable of handling the nonconvex Boolean constraintsOctober 12, 2012 DRAFT
  • 21. 21explicitly by a simple projection onto a discrete nonconvex setC := x | 1Tx = Nl, xi ∈ {0, 1}, i = 1, . . . , n . (15)We can rewrite (LS1) as an unconstrained optimization problemminimizeX, xJ(x) + I(x) (16)where I(x) is indicator function associated with set CI(x) =0 if x ∈ C+ ∞ if x /∈ C.Now, (16) can be put into the following equivalent form suitable for the application of ADMMminimizex, zJ(x) + I(z)subject to x − z = 0(17)and the augmented Lagrangian associated with (17) is given byLρ(x, z, λ) = J(x) + I(z) + λT(x − z) +ρ2x − z 22where λ ∈ Rnis the dual variable and ρ is a positive number. For k = 0, 1, . . ., the ADMMalgorithm updates x, z, and λ in an iterative fashionxk+1:= arg minxLρ(x, zk, λk) (18a)zk+1:= arg minzLρ(xk+1, z, λk) (18b)λk+1:= λk+ ρ(xk+1− zk+1) (18c)until xk+1− zk+12 ≤ and zk+1− zk2 ≤ .Splitting the optimization variables into two copies {x, z} and updating them in an alternatingfashion yields the minimization problems (18a) and (18b) that are easy to solve.1) x-minimization step: By completion of squares in Lρ with respect to x, problem (18a) canbe expressed asminimizextrace (L + DκDx)−1+ρ2x − uk 22 (19)October 12, 2012 DRAFT
  • 22. 22whereuk:= zk− (1/ρ)λk.Since (19) is equivalent to the following problem,minimizex, µtrace ((L + DκDx)−1) + µsubject toρ2x − uk 22 ≤ µit can be expressed as an SDPminimizeX, x, µtrace (X) + µsubject toX II L + DκDx 0I x − uk(x − uk)T2µ/ρ 0where the second LMI, resulting from the use of Schur complement, is an alternative way ofwriting the quadratic constraint2µ/ρ − (x − uk)T(x − uk) ≥ 0.Thus, for small networks, problem (19) can be solved efficiently using standard SDP solvers.For large networks, we use descent methods [32] (e.g., Newton’s method) with the gradient andHessian of Lρ with respect to x being given byLρ = − κ ◦ diag ((L + DκDx)−2) + ρ (x − uk)2Lρ = 2 (Dκ(L + DκDx)−2Dκ) ◦ (L + DκDx)−1+ ρIwhere diag (M) denotes the vector determined by the main diagonal of a matrix M.2) z-minimization step: Using similar argument as in [17, Section 9.1] (see Appendix D fordetails), the z-minimization problem (18b) can be solved explicitly using a simple projectiononto the set Czi =1 if vki ≥ [vk]Nl0 if vki < [vk]Nl(20)October 12, 2012 DRAFT
  • 23. 23TABLE II: Lower and upper bounds on the noise-corrupted leader selection problem (LS1) for theexample shown in Fig. 1. Lower bounds Jlb are obtained by solving the convex relaxation (CR);upper bounds Jub from greedy algorithm – the one-leader-at-a-time algorithm followed by theswap algorithm – are actually tight, i.e., Jub = Jopt; upper bounds Jub from ADMM are tightfor Nl = 4, 5.greedy algorithm ADMMNl Jlb Jub leaders Jub leaders1 38.4 72.3 13 118.3 252 30.3 43.4 8, 25 47.9 7, 253 26.7 35.2 8, 16, 25 36.7 7, 16, 254 24.3 30.0 3, 7, 16, 25 30.0 3, 7, 16, 255 22.4 25.8 3, 7, 9, 16, 25 25.8 3, 7, 9, 16, 25wherevk:= xk+1+ (1/ρ)λkand [vk]Nldenotes the (Nl)th largest entry of vk. We note that reference [17] provides relatedprojections onto several important nonconvex sets.D. ExamplesWe next provide several examples to illustrate the performance of the developed methods. Inall examples we set κi to be the degree of node i. We set the initial conditions of the ADMMalgorithm to {z0= 0, λ0= 0} and the penalty weight to ρ = 103.1) A small network from [18]: For the example discussed in Section III-C1 with Nl ≤ 5, wedetermine the global minima to the noise-corrupted leader selection problem (LS1) by exhaustivesearch. It turns out that the one-leader-at-a-time algorithm followed by the swap algorithmactually finds the global minima. As shown in Table II, ADMM provides the global minimafor the problems with 4 and 5 leaders. It is also worth mentioning that the globally optimalselection of noise-corrupted leaders coincides with the globally optimal selection of noise-freeleaders (cf. Table I).Figure 9a shows lower bounds resulting from convex relaxation and upper bounds resultingfrom ADMM and from greedy algorithm. As the number of leaders Nl increases, the gap betweenthe lower and upper bounds from greedy algorithm decreases; see Fig. 9b.October 12, 2012 DRAFT
  • 24. 24(a) (b)Fig. 9: The network with 25 nodes: (a) lower bounds (−) resulting from convex relaxation andupper bounds resulting from greedy algorithm (i.e., one-leader-at-a-time algorithm followed byswap algorithm) (+) and from ADMM (◦); (b) the gap between lower bounds and upper boundsresulting from greedy algorithm.2) A 2D lattice: We next consider the leader selection problem for a 9 × 9 regular lattice.Figure 10a shows lower bounds resulting from convex relaxation and upper bounds resultingfrom ADMM and from greedy algorithm, i.e., the one-leader-at-a-time algorithm followed bythe swap algorithm. As the number of leaders Nl increases, the gap between the lower and upperbounds from greedy algorithm decreases; see Fig. 10b. For Nl = 1, . . . , 40, the number of swapupdates ranges between 1 and 19 and the average number of swaps is 10.Figure 11 shows selection of leaders resulting from the greedy algorithm for different choicesof Nl. For Nl = 1, the center node (5, 5) provides the optimal selection of a single leader. As Nlincreases, nodes away from the center node (5, 5) are selected; for example, for Nl = 2, nodes{(3, 3), (7, 7)} are selected and for Nl = 3, nodes {(2, 6), (6, 2), (8, 8)} are selected. Selectionof nodes farther away from the center becomes more significant for Nl = 4 and Nl = 8.The selection of leaders exhibits symmetry shown in Fig. 11. In particular, when Nl is large,almost uniform spacing between the leaders is observed; see Fig. 11f for Nl = 40. This is incontrast to the selection of leaders along the boundary nodes in the random network example inFig. 6c. For the random network example in Section III-C2, the selection of the noise-corruptedleaders resembles that of the noise-free leaders (results are omitted for brevity).October 12, 2012 DRAFT
  • 25. 25(a) (b)Fig. 10: A 2D lattice: (a) lower bounds (−) resulting from convex relaxation and upperbounds resulting from greedy algorithm (i.e., one-leader-at-a-time algorithm followed by swapalgorithm) (+) and from ADMM (◦); (b) the gap between lower bounds and upper boundsresulting from greedy algorithm.(a) Nl = 1 (b) Nl = 2 (c) Nl = 3(d) Nl = 4 (e) Nl = 8 (f) Nl = 40Fig. 11: Selections of leaders (•) obtained using the one-at-a-time algorithm followed by theswap algorithm for a 2D lattice. The two selections of two leaders denoted by (•) and (∗) in (b)provide the same objective function J. The four selections of three leaders denoted by (•), (∗),(×), and (◦) in (c) provide the same J.October 12, 2012 DRAFT
  • 26. 26V. CONCLUDING REMARKSThe main contribution of this paper is the development of efficient algorithms that facilitateselection of leaders in large stochastically forced consensus networks. For the noise-corruptedleader selection problem (LS1), we focus on computing lower and upper bounds on the globaloptimal value. A lower bound is obtained by solving a convex relaxation, and upper bounds resultfrom a simple but efficient greedy algorithm and the alternating direction method of multipliers.For the noise-free leader selection problem (LS2), we provide an explicit expression for thevariance amplification of the network. This allows us to identify sources of nonconvexity and topropose a convex relaxation of the objective function in (LS2). Furthermore, we use augmentationof the objective function with the 1 norm of the vector of optimization variables as a surrogatefor obtaining a sparse solution whose nonzero elements identify the leaders. Several examples areprovided to illustrate the effectiveness of our algorithms. We are currently applying these tools forleader selection problems in different types of networks, including small-world social networks.APPENDIXA. Equivalence between leader selection and sensor selection problemsWe next show that the problem of choosing Nl absolute position measurements among nsensors to minimize the variance of the estimation error in Section II-B is equivalent to thenoise-corrupted leader selection problem (LS1).Given the measurement vector y in (5), the linear minimum variance unbiased estimate of ψis determined by [41, Chapter 4.4]ˆψ = (ErW−1r ETr + Ea(ETa WaEa)−1ETa )−1(ErW−1r yr + Ea(ETa WaEa)−1ya)with the covariance of the estimation errorΣ = E((ψ − ˆψ)(ψ − ˆψ)T) = (ErW−1r ETr + Ea(ETa WaEa)−1ETa )−1.Furthermore, let us assume thatWr = I, Wa = D−1κ .The choice of Wa indicates that a larger value of κi corresponds to a more accurate absoluteOctober 12, 2012 DRAFT
  • 27. 27measurement of sensor i. Then(ETa WaEa)−1= (ETa D−1κ Ea)−1= ETa DκEaand thus,Σ = (ErETr + EaETa DκEaETa )−1.Since EaETa is a diagonal matrix with its ith diagonal element being 1 for i ∈ Ia and ErETr isthe Laplacian matrix of the relative measurement graph, it follows thatDx = EaETa , L = ErETr , Σ = (L + DxDκDx)−1= (L + DκDx)−1where DxDκDx = DκDx because Dx and Dκ commute and DxDx = Dx. Therefore, we haveestablished equivalence between the noise-corrupted leader selection problem (LS1) and theproblem of choosing Nl sensors with absolute position measurements such that the variance ofthe estimation error is minimized.To formulate an estimation problem that is equivalent to the noise-free leader selection prob-lem (LS2), we follow [10] and assume that the positions of Nl sensors are known a priori. Letψl denote the positions of these reference sensors and let ψf denote the positions of the othersensors. We can thus write the relative measurement equation (4) asyr = ETr ψ + wr = ETl ψl + ETf ψf + wrand the linear minimum variance unbiased estimate of ψf is given byˆψf = (Ef ETf )−1Ef W−1r (yr − ETl ψl)with covariance of the estimation errorΣf = (Ef ETf )−1.Identifying Ef ETf with Lf in the Laplacian matrixL = ErETr =ElETl ElETfEf ETl Ef ETf =Ll L0LT0 LfOctober 12, 2012 DRAFT
  • 28. 28establishes equivalence between problem (LS2) and the problem of assigning Nl sensors withknown reference positions to minimize the variance of the estimation error of sensor network.B. ADMM for the soft constraint methodWe next employ ADMM for the soft constraint method developed in Section III-B. We considerthe following minimization problemminimizexf(x) + γ x 1where f is the convex approximation of (6)f(x) = trace (I − Dx)(G0 + Dx ◦ L)−1(I − Dx)and G0 is the linear approximation of G given by (10). This problem is equivalent to theconstrained problemminimizex, zf(x) + γ z 1subject to x − z = 0and the associated augmented Lagrangian function is given byLρ(x, z, λ) = f(x) + γ z 1 + λT(x − z) +ρ2x − z 22.By completion of squares in Lρ with respect to z, the z-minimization problem (18b) can beexpressed asminimizezγ z 1 +ρ2z − vk 22where vk= xk+1+(1/ρ)λk. The solution is given by the soft thresholding operator (e.g., see [17,Section 4.4.3])z∗i = Sγ/ρ(vki ) =1 −γ/ρ|vki |vki , |vki | > γ/ρ0, |vki | ≤ γ/ρ(21)for i = 1, . . . , n. On the other hand, by completing squares in Lρ with respect to x, we obtainminimizexφ(x) = f(x) +ρ2x − uk 22October 12, 2012 DRAFT
  • 29. 29where uk= zk− (1/ρ)λk. This problem can be solved using descent methods (e.g., gradientmethod [32]). Here, we provide the expression for the gradient of φφ(x) = − 2 diag ((I − Dx)M−1) + diag (L(I − Dx0 )M−1(I − Dx)2M−1)− diag (M−1(I − Dx)2M−1) ◦ diag (L) + ρ(x − uk)where M = G0 + Dx ◦ L.C. Customized interior point method for (CR)We begin by augmenting the objective function in (CR) with log-barrier functions associatedwith the inequality constraints on ximinimizexq(x) = τ trace (L + DκDx)−1+ni = 1− log(xi) − log(1 − xi)subject to 1Tx = Nl.(22)The solution of the approximate problems (22) converges to the solution of the convex relax-ation (CR) as the positive scalar τ increases to infinity [32, Section 11.2]. We solve a sequenceof problem (22) by gradually increasing τ, and by starting each minimization using the solutionfrom the previous value of τ. We use Newton’s method to solve (22) for a fixed τ, and theNewton direction for problems with linear constraints is given by (e.g., see [32, Section 10.2])xnt = − ( 2q)−1q − δ( 2q)−11whereδ = −1T( 2q)−1q1T ( 2q)−11.Here, the expressions for the ith entry of the gradient direction q and for the Hessian matrixare given by( q)i = − τ κi ((L + DκDx)−2)ii − x−1i − (xi − 1)−12q = 2τ (Dκ(L + DκDx)−2Dκ) ◦ (L + DκDx)−1+ diag x−2i + (1 − xi)−2.We next examine complexity of computing the Newton direction xnt. The major cost ofcomputing 2q is to form (L+DκDx)−2, which takes (7/3)n3operations to form (L+DκDx)−1October 12, 2012 DRAFT
  • 30. 30and n3operations to form (L + DκDx)−2. Computing xnt requires solving two linear equations,( 2q) y = − q, ( 2q) z = −1which takes (1/3)n3operations using Cholesky factorization. Thus, computation of each Newtonstep requires (7/3 + 1 + 1/3)n3= (11/3)n3operations.D. Derivation of (20)We use completion of squares to obtain the following problem which is equivalent to (18b)minimizez(ρ/2) z − vk 22subject to z ∈ Cwhere vk= xk+1+ (1/ρ)λkand the set C is given by (15). Projecting v onto C yieldszi =1 if vki ≥ [vk]Nl0 if vki < [vk]Nl(23)where [vk]Nlis the (Nl)th largest entry of vk. To see this, consider ¯z ∈ C, i.e., 1T¯z = Nl and¯zi ∈ {0, 1}, but ¯z is not the projection determined by (23). Thus, there exists at least one entryof ¯z, say the rth entry, such that ¯zr = 1 for vkr < [vk]Nl, and at least one entry, say the jth entry,such that ¯zj = 0 for vkj ≥ [vk]Nl. Considerδrj = (¯zr − vkr )2+ (¯zj − vkj )2= (1 − vkr )2+ (vkj )2and δjr = (vkr )2+ (1 − vkj )2. Since δrj − δjr = 2(vkj − vkr ) > 0, it follows that the objectivefunction (ρ/2) z−vk 22 will decrease if we choose {¯zr = 0, ¯zj = 1} instead of {¯zr = 1, ¯zj = 0}.Therefore, we can reduce the objective function by exchanging the values of two entries ¯zr = 1(with vkr < [vk]Nl) and ¯zj = 0 (with vkj ≥ [vk]Nl) until (23) is satisfied for all i = 1, . . . , n.REFERENCES[1] M. Mesbahi and M. Egerstedt, Graph-theoretic Methods in Multiagent Networks. Princeton University Press, 2010.[2] M. H. DeGroot, “Reaching a consensus,” J. Amer. Statist. Assoc., vol. 69, no. 345, pp. 118–121, 1974.[3] B. Golub and M. Jackson, “Naive learning social networks and the wisdom of crowds,” American Economic Journal:Microeconomics, vol. 2, no. 1, pp. 112–149, 2010.October 12, 2012 DRAFT
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