Input Matrix Construction and Approximation Using a Graphic Approach

Given a state transition matrix (STM), we reinvestigate the problem of constructing the sparest input matrix with a fixed number of inputs to guarantee controllability. We give a new and simple graph theoretic characterization for the sparsity pattern of input matrices to guarantee controllability for a general STM admitting multiple eigenvalues, and provide a deterministic procedure with polynomial time complexity to construct real valued input matrices with arbi- trarily prescribed sparsity pattern satisfying controllability. Based on this criterion, some novel results on sparsely controlling a system are obtained. It is proven that the minimal number of inputs to guarantee controllability equals to the maximum geometric multiplicity of the STM under the constraint that some states are actuated-forbidden, extending the results of [28]. The minimal sparsity of input matrices with a fixed number of inputs is not necessarily equal to the minimal number of actuated states to ensure controllability. Furthermore, a graphic sub- modular function is built, leading to a greedy algorithm to efficiently approximate the minimal actuated states to assure controllability for general STMs. For the problem of approximating the sparsest input matrices with a fixed number of inputs, we propose a simple greedy algo- rithm (non-submodular) and a two-stage algorithm, and demonstrate that the latter algorithm, inspired from techniques in dynamic coloring, has a provable approximation guarantee. Finally, we present numerical results to show the efficiency and effectiveness of our approaches.Comment: to appear in International Journal of Contro

Structural Controllability of a Networked Dynamic System with LFT Parameterized Subsystems

This paper studies structural controllability for a networked dynamic system (NDS), in which each subsystem may have different dynamics, and unknown parameters may exist both in subsystem dynamics and in subsystem interconnections. In addition, subsystem parameters are parameterized by a linear fractional transformation (LFT). It is proven that controllability keeps to be a generic property for this kind of NDSs. Some necessary and sufficient conditions are then established respectively for them to be structurally controllable, to have a fixed uncontrollable mode, and to have a parameter dependent uncontrollable mode, under the condition that each subsystem interconnection link can take a weight independently. These conditions are scalable, and in their verifications, all arithmetic calculations are performed separately on each subsystem. In addition, these conditions also reveal influences on NDS controllability from subsystem input-output relations, subsystem uncontrollable modes and subsystem interconnection topology. Based on these observations, the problem of selecting the minimal number of subsystem interconnection links is studied under the requirement of constructing a structurally controllable NDS. A heuristic method is derived with some provable approximation bounds and a low computational complexity.Comment: Accepted by IEEE Transactions on Automatic Control as full paper, scheduled to appear in Volume 64 (2019), Issue 12 (December

An optimal consensus tracking control algorithm for autonomous underwater vehicles with disturbances

The optimal disturbance rejection control problem is considered for consensus tracking systems affected by external persistent disturbances and noise. Optimal estimated values of system states are obtained by recursive filtering for the multiple autonomous underwater vehicles modeled to multi-agent systems with Kalman filter. Then the feedforward-feedback optimal control law is deduced by solving the Riccati equations and matrix equations. The existence and uniqueness condition of feedforward-feedback optimal control law is proposed and the optimal control law algorithm is carried out. Lastly, simulations show the result is effectiveness with respect to external persistent disturbances and noise

C^1-Regularity of planar \infty-harmonic functions - REVISIT

In the seminal paper [Arch. Ration. Mech. Anal. 176 (2005), 351--361], Savin proved the $C^1$-regularity of planar $\infty$-harmonic functions $u$. Here we give a new understanding of it from a capacity viewpoint and drop several high technique arguments therein. Our argument is essentially based on a topological lemma of Savin, a flat estimate by Evans and Smart, % \cite{es11a}, $W^{1,2}_{loc}$-regularity of $|Du|$ and Crandall's flow for infinity harmonic functions.Comment: 6 page

Adaptive Double-Exploration Tradeoff for Outlier Detection

We study a variant of the thresholding bandit problem (TBP) in the context of outlier detection, where the objective is to identify the outliers whose rewards are above a threshold. Distinct from the traditional TBP, the threshold is defined as a function of the rewards of all the arms, which is motivated by the criterion for identifying outliers. The learner needs to explore the rewards of the arms as well as the threshold. We refer to this problem as "double exploration for outlier detection". We construct an adaptively updated confidence interval for the threshold, based on the estimated value of the threshold in the previous rounds. Furthermore, by automatically trading off exploring the individual arms and exploring the outlier threshold, we provide an efficient algorithm in terms of the sample complexity. Experimental results on both synthetic datasets and real-world datasets demonstrate the efficiency of our algorithm

Collaborative Learning with Limited Interaction: Tight Bounds for Distributed Exploration in Multi-Armed Bandits

Best arm identification (or, pure exploration) in multi-armed bandits is a fundamental problem in machine learning. In this paper we study the distributed version of this problem where we have multiple agents, and they want to learn the best arm collaboratively. We want to quantify the power of collaboration under limited interaction (or, communication steps), as interaction is expensive in many settings. We measure the running time of a distributed algorithm as the speedup over the best centralized algorithm where there is only one agent. We give almost tight round-speedup tradeoffs for this problem, along which we develop several new techniques for proving lower bounds on the number of communication steps under time or confidence constraints.Comment: 33 page

Tight Bounds for Collaborative PAC Learning via Multiplicative Weights

We study the collaborative PAC learning problem recently proposed in Blum et al.~\cite{BHPQ17}, in which we have $k$ players and they want to learn a target function collaboratively, such that the learned function approximates the target function well on all players' distributions simultaneously. The quality of the collaborative learning algorithm is measured by the ratio between the sample complexity of the algorithm and that of the learning algorithm for a single distribution (called the overhead). We obtain a collaborative learning algorithm with overhead $O(\ln k)$, improving the one with overhead $O(\ln^2 k)$ in \cite{BHPQ17}. We also show that an $\Omega(\ln k)$ overhead is inevitable when $k$ is polynomial bounded by the VC dimension of the hypothesis class. Finally, our experimental study has demonstrated the superiority of our algorithm compared with the one in Blum et al. on real-world datasets.Comment: Accepted to NIPS 2018. 14 page

Adaptive Multiple-Arm Identification

We study the problem of selecting $K$ arms with the highest expected rewards in a stochastic $n$-armed bandit game. This problem has a wide range of applications, e.g., A/B testing, crowdsourcing, simulation optimization. Our goal is to develop a PAC algorithm, which, with probability at least $1-\delta$, identifies a set of $K$ arms with the aggregate regret at most $\epsilon$. The notion of aggregate regret for multiple-arm identification was first introduced in \cite{Zhou:14} , which is defined as the difference of the averaged expected rewards between the selected set of arms and the best $K$ arms. In contrast to \cite{Zhou:14} that only provides instance-independent sample complexity, we introduce a new hardness parameter for characterizing the difficulty of any given instance. We further develop two algorithms and establish the corresponding sample complexity in terms of this hardness parameter. The derived sample complexity can be significantly smaller than state-of-the-art results for a large class of instances and matches the instance-independent lower bound upto a $\log(\epsilon^{-1})$ factor in the worst case. We also prove a lower bound result showing that the extra $\log(\epsilon^{-1})$ is necessary for instance-dependent algorithms using the introduced hardness parameter.Comment: 30 pages, 5 figures, preliminary version to appear in ICML 201

The limit of the m-norms of a class of symmetric matrices and its applications

We consider a special symmetric matrix and obtain a similar formula as the one obtained by Weyl's criterion. Some applications of the formula are given, where we give a new way to calculate the integral of $\ln\Gamma(x)$ on $[0,1]$, and we claim that one class of matrices are not Hadamard matrices.Comment: 10 pages, 1 figur

Some sharp Sobolev regularity for inhomogeneous ∞\infty-Laplace equation in plane

Suppose $\Omega\Subset \mathbb R^2$ and $f\in BV_{loc}(\Omega)\cap C^0(\Omega)$ with $|f|>0$ in $\Omega$. Let $u\in C^0(\Omega)$ be a viscosity solution to the inhomogeneous $\infty$-Laplace equation $$-\Delta_{\infty} u :=-\frac12\sum_{i=1}^2(|Du|^2)_iu_i= -\sum_{i,j=1}^2u_iu_ju_{ij} =f \quad {\rm in}\ \Omega.$$ The following are proved in this paper. (i) For $\alpha > 3/2$, we have $|Du|^{\alpha}\in W^{1,2}_{loc}(\Omega)$, which is (asymptotic) sharp when $\alpha \to 3/2$. Indeed, the function $w(x_1,x_2)=-x_1^ {4/3}$ is a viscosity solution to $-\Delta_\infty w=\frac{4^3}{3^4}$ in $\mathbb R^2$. For any $p> 2$, $|Dw|^\alpha \notin W^{1,p}_{loc}(\mathbb R^2)$ whenever $\alpha\in(3/2,3-3/p)$. (ii) For $\alpha \in(0, 3/2]$ and $p\in[1, 3/(3-\alpha))$, we have $|Du|^{\alpha}\in W^{1,p}_{loc}(\Omega)$, which is sharp when $p\to 3/(3-\alpha)$. Indeed, $|Dw|^\alpha \notin W^{1,3/(3-\alpha)}_{loc}(\mathbb R^2)$. (iii) For $\epsilon > 0$, we have $|Du|^{-3+\epsilon }\in L^1_{loc}(\Omega )$, which is sharp when $\epsilon\to0$. Indeed, $|Dw|^{-3} \notin L^1_{loc}(\mathbb R^2)$. (iv) For $\alpha > 0$, we have -(|Du|^{\alpha})_iu_i= 2\alpha|Du|^{{ \alpha-2}}f \ \mbox{ almost everywhere in $\Omega$}. Some quantative bounds are also given