Showing papers on "Bounded function published in 2011"

Bayesian Learning in Social Networks

Abstract: We study the (perfect Bayesian) equilibrium of a sequential learning model over a general social network. Each individual receives a signal about the underlying state of the world, observes the past actions of a stochastically generated neighbourhood of individuals, and chooses one of two possible actions. The stochastic process generating the neighbourhoods defines the network topology. We characterize pure strategy equilibria for arbitrary stochastic and deterministic social networks and characterize the conditions under which there will be asymptotic learning—convergence (in probability) to the right action as the social network becomes large. We show that when private beliefs are unbounded (meaning that the implied likelihood ratios are unbounded), there will be asymptotic learning as long as there is some minimal amount of “expansion in observations”. We also characterize conditions under which there will be asymptotic learning when private beliefs are bounded.

A Novel Data-Driven Control Approach for a Class of Discrete-Time Nonlinear Systems

Abstract: In this work, a novel data-driven control approach, model-free adaptive control, is presented based on a new dynamic linearization technique for a class of discrete-time single-input and single-output nonlinear systems. The main feature of the approach is that the controller design depends merely on the input and the output measurement data of the controlled plant. The theoretical analysis shows that the approach guarantees the bounded input and bounded output stability and tracking error monotonic convergence. The comparison experiments verify the effectiveness of the proposed approach.

Metric measure spaces with Riemannian Ricci curvature bounded from below

Abstract: In this paper we introduce a synthetic notion of Riemannian Ricci bounds from below for metric measure spaces (X,d,m) which is stable under measured Gromov-Hausdorff convergence and rules out Finsler geometries. It can be given in terms of an enforcement of the Lott, Sturm and Villani geodesic convexity condition for the entropy coupled with the linearity of the heat flow. Besides stability, it enjoys the same tensorization, global-to-local and local-to-global properties. In these spaces, that we call RCD(K,\infty) spaces, we prove that the heat flow (which can be equivalently characterized either as the flow associated to the Dirichlet form, or as the Wasserstein gradient flow of the entropy) satisfies Wasserstein contraction estimates and several regularity properties, in particular Bakry-Emery estimates and the L^\infty-Lip Feller regularization. We also prove that the distance induced by the Dirichlet form coincides with d, that the local energy measure has density given by the square of Cheeger's relaxed slope and, as a consequence, that the underlying Brownian motion has continuous paths. All these results are obtained independently of Poincare and doubling assumptions on the metric measure structure and therefore apply also to spaces which are not locally compact, as the infinite-dimensional ones

Finite-Time Attitude Tracking Control for Spacecraft Using Terminal Sliding Mode and Chebyshev Neural Network

Abstract: A finite-time attitude tracking control scheme is proposed for spacecraft using terminal sliding mode and Chebyshev neural network (NN) (CNN). The four-parameter representations (quaternion) are used to describe the spacecraft attitude for global representation without singularities. The attitude state (i.e., attitude and velocity) error dynamics is transformed to a double integrator dynamics with a constraint on the spacecraft attitude. With consideration of this constraint, a novel terminal sliding manifold is proposed for the spacecraft. In order to guarantee that the output of the NN used in the controller is bounded by the corresponding bound of the approximated unknown function, a switch function is applied to generate a switching between the adaptive NN control and the robust controller. Meanwhile, a CNN, whose basis functions are implemented using only desired signals, is introduced to approximate the desired nonlinear function and bounded external disturbances online, and the robust term based on the hyperbolic tangent function is applied to counteract NN approximation errors in the adaptive neural control scheme. Most importantly, the finite-time stability in both the reaching phase and the sliding phase can be guaranteed by a Lyapunov-based approach. Finally, numerical simulations on the attitude tracking control of spacecraft in the presence of an unknown mass moment of inertia matrix, bounded external disturbances, and control input constraints are presented to demonstrate the performance of the proposed controller.

Analytic regularity and polynomial approximation of parametric and stochastic elliptic pde's

Abstract: Parametric partial differential equations are commonly used to model physical systems. They also arise when Wiener chaos expansions are used as an alternative to Monte Carlo when solving stochastic elliptic problems. This paper considers a model class of second order, linear, parametric, elliptic PDE's in a bounded domain D with coefficients depending on possibly countably many parameters. It shows that the dependence of the solution on the parameters in the diffusion coefficient is analytically smooth. This analyticity is then exploited to prove that under very weak assumptions on the diffusion coefficients, the entire family of solutions to such equations can be simultaneously approximated by multivariate polynomials (in the parameters) with coefficients taking values in the Hilbert space of weak solutions of the elliptic problem with a controlled number of terms N. The convergence rate in terms of N does not depend on the number of parameters in V which may be countable, therefore breaking the curse of dimensionality. The discretization of the coefficients from a family of continuous, piecewise linear finite element functions in D is shown to yield finite dimensional approximations whose convergence rate in terms of the overall number Ndof of degrees of freedom is the minimum of the convergence rates afforded by the best N-term sequence approximations in the parameter space and the rate of finite element approximations in D for a single instance of the parametric problem.

Bounded biharmonic weights for real-time deformation

Abstract: Object deformation with linear blending dominates practical use as the fastest approach for transforming raster images, vector graphics, geometric models and animated characters. Unfortunately, linear blending schemes for skeletons or cages are not always easy to use because they may require manual weight painting or modeling closed polyhedral envelopes around objects. Our goal is to make the design and control of deformations simpler by allowing the user to work freely with the most convenient combination of handle types. We develop linear blending weights that produce smooth and intuitive deformations for points, bones and cages of arbitrary topology. Our weights, called bounded biharmonic weights, minimize the Laplacian energy subject to bound constraints. Doing so spreads the influences of the controls in a shape-aware and localized manner, even for objects with complex and concave boundaries. The variational weight optimization also makes it possible to customize the weights so that they preserve the shape of specified essential object features. We demonstrate successful use of our blending weights for real-time deformation of 2D and 3D shapes.

X -Armed Bandits

Abstract: We consider a generalization of stochastic bandits where the set of arms, X, is allowed to be a generic measurable space and the mean-payoff function is "locally Lipschitz" with respect to a dissimilarity function that is known to the decision maker. Under this condition we construct an arm selection policy, called HOO (hierarchical optimistic optimization), with improved regret bounds compared to previous results for a large class of problems. In particular, our results imply that if X is the unit hypercube in a Euclidean space and the mean-payoff function has a finite number of global maxima around which the behavior of the function is locally continuous with a known smoothness degree, then the expected regret of HOO is bounded up to a logarithmic factor by √n, that is, the rate of growth of the regret is independent of the dimension of the space. We also prove the minimax optimality of our algorithm when the dissimilarity is a metric. Our basic strategy has quadratic computational complexity as a function of the number of time steps and does not rely on the doubling trick. We also introduce a modified strategy, which relies on the doubling trick but runs in linearithmic time. Both results are improvements with respect to previous approaches.

Bounded $H_{\infty}$ Synchronization and State Estimation for Discrete Time-Varying Stochastic Complex Networks Over a Finite Horizon

Abstract: In this paper, new synchronization and state estimation problems are considered for an array of coupled discrete time-varying stochastic complex networks over a finite horizon. A novel concept of bounded H∞ synchronization is proposed to handle the time-varying nature of the complex networks. Such a concept captures the transient behavior of the time-varying complex network over a finite horizon, where the degree of bounded synchronization is quantified in terms of the H∞-norm. A general sector-like nonlinear function is employed to describe the nonlinearities existing in the network. By utilizing a timevarying real-valued function and the Kronecker product, criteria are established that ensure the bounded H∞ synchronization in terms of a set of recursive linear matrix inequalities (RLMIs), where the RLMIs can be computed recursively by employing available MATLAB toolboxes. The bounded H∞ state estimation problem is then studied for the same complex network, where the purpose is to design a state estimator to estimate the network states through available output measurements such that, over a finite horizon, the dynamics of the estimation error is guaranteed to be bounded with a given disturbance attenuation level. Again, an RLMI approach is developed for the state estimation problem. Finally, two simulation examples are exploited to show the effectiveness of the results derived in this paper.

Index of Elliptic Operators

Abstract: Ellipticity condition, proper ellipticity and Lopatinskii condition imply the Fredholm property of elliptic problems in bounded domains In addition, invertibility of limiting problems determines the Fredholm property and solvability conditions of elliptic problems in unbounded domains If this property is satisfied, then the index of the operator is defined There is an extensive literature devoted to the index of elliptic operators in bounded domains and for some classes of operators in unbounded domains (see the bibliographical comments)

The KL-UCB Algorithm for Bounded Stochastic Bandits and Beyond

Abstract: This paper presents a finite-time analysis of the KL-UCB algorithm, an online, horizon-free index policy for stochastic bandit problems. We prove two distinct results: first, for arbitrary bounded rewards, the KL-UCB algorithm satisfies a uniformly better regret bound than UCB or UCB2; second, in the special case of Bernoulli rewards, it reaches the lower bound of Lai and Robbins. Furthermore, we show that simple adaptations of the KL-UCB algorithm are also optimal for specific classes of (possibly unbounded) rewards, including those generated from exponential families of distributions. A large-scale numerical study comparing KL-UCB with its main competitors (UCB, UCB2, UCB-Tuned, UCB-V, DMED) shows that KL-UCB is remarkably efficient and stable, including for short time horizons. KL-UCB is also the only method that always performs better than the basic UCB policy. Our regret bounds rely on deviations results of independent interest which are stated and proved in the Appendix. As a by-product, we also obtain an improved regret bound for the standard UCB algorithm.

The KL-UCB Algorithm for Bounded Stochastic Bandits and Beyond

Abstract: This paper presents a nite-time analysis of the KL-UCB algorithm, an online, horizonfree index policy for stochastic bandit problems. We prove two distinct results: rst, for arbitrary bounded rewards, the KL-UCB algorithm satises a uniformly better regret bound than UCB and its variants; second, in the special case of Bernoulli rewards, it reaches the lower bound of Lai and Robbins. Furthermore, we show that simple adaptations of the KL-UCB algorithm are also optimal for specic classes of (possibly unbounded) rewards, including those generated from exponential families of distributions. A large-scale numerical study comparing KL-UCB with its main competitors (UCB, MOSS, UCB-Tuned, UCB-V, DMED) shows that KL-UCB is remarkably ecient and stable, including for short time horizons. KL-UCB is also the only method that always performs better than the basic UCB policy. Our regret bounds rely on deviations results of independent interest which are stated and proved in the Appendix. As a by-product, we also obtain an improved regret bound for the standard UCB algorithm.

Wavenumber Explicit Convergence Analysis for Galerkin Discretizations of the Helmholtz Equation

Abstract: We develop a stability and convergence theory for a class of highly indefinite elliptic boundary value problems (bvps) by considering the Helmholtz equation at high wavenumber $k$ as our model problem. The key element in this theory is a novel $k$-explicit regularity theory for Helmholtz bvps that is based on decomposing the solution into two parts: the first part has the Sobolev regularity properties expected of second order elliptic PDEs but features $k$-independent regularity constants; the second part is an analytic function for which $k$-explicit bounds for all derivatives are given. This decomposition is worked out in detail for several types of bvps, namely, the Helmholtz equation in bounded smooth domains or convex polygonal domains with Robin boundary conditions and in exterior domains with Dirichlet boundary conditions. We present an error analysis for the classical $hp$-version of the finite element method ($hp$-FEM) where the dependence on the mesh width $h$, the approximation order $p$, and the wavenumber $k$ is given explicitly. In particular, under the assumption that the solution operator for Helmholtz problems is polynomially bounded in $k$, it is shown that quasi optimality is obtained under the conditions that $kh/p$ is sufficiently small and the polynomial degree $p$ is at least O(log $k$).

Bounded biharmonic weights for real-time deformation

Abstract: Object deformation with linear blending dominates practical use as the fastest approach for transforming raster images, vector graphics, geometric models and animated characters. Unfortunately, lin...

Adaptive Neural Output Feedback Controller Design With Reduced-Order Observer for a Class of Uncertain Nonlinear SISO Systems

Abstract: An adaptive output feedback control is studied for uncertain nonlinear single-input-single-output systems with partial unmeasured states. In the scheme, a reduced-order observer (ROO) is designed to estimate those unmeasured states. By employing radial basis function neural networks and incorporating the ROO into a new backstepping design, an adaptive output feedback controller is constructively developed. A prominent advantage is its ability to balance the control action between the state feedback and the output feedback. In addition, the scheme can be still implemented when all the states are not available. The stability of the closed-loop system is guaranteed in the sense that all the signals are semiglobal uniformly ultimately bounded and the system output tracks the reference signal to a bounded compact set. A simulation example is given to validate the effectiveness of the proposed scheme.

Cause clue clauses: error localization using maximum satisfiability

Abstract: Much effort is spent by programmers everyday in trying to reduce long, failing execution traces to the cause of the error. We present an algorithm for error cause localization based on a reduction to the maximal satisfiability problem (MAX-SAT), which asks what is the maximum number of clauses of a Boolean formula that can be simultaneously satisfied by an assignment. At an intuitive level, our algorithm takes as input a program and a failing test, and comprises the following three steps. First, using bounded model checking, and a bound obtained from the execution of the test, we encode the semantics of a bounded unrolling of the program as a Boolean trace formula. Second, for a failing program execution (e.g., one that violates an assertion or a post-condition), we construct an unsatisfiable formula by taking the formula and additionally asserting that the input is the failing test and that the assertion condition does hold at the end. Third, using MAX-SAT, we find a maximal set of clauses in this formula that can be satisfied together, and output the complement set as a potential cause of the error.We have implemented our algorithm in a tool called BugAssist that performs error localization for C programs. We demonstrate the effectiveness of BugAssist on a set of benchmark examples with injected faults, and show that in most cases, BugAssist can quickly and precisely isolate a few lines of code whose change eliminates the error. We also demonstrate how our algorithm can be modified to automatically suggest fixes for common classes of errors such as off-by-one.We have implemented our algorithm in a tool called BugAssist that performs error localization for C programs. We demonstrate the effectiveness of BugAssist on a set of benchmark examples with injected faults, and show that in most cases, BugAssist can quickly and precisely isolate a few lines of code whose change eliminates the error. We also demonstrate how our algorithm can be modified to automatically suggest fixes for common classes of errors such as off-by-one.

Global solutions in a fully parabolic chemotaxis system with singular sensitivity

Abstract: The Neumann boundary value problem for the chemotaxis system is considered in a smooth bounded domain Ω⊂ℝn, n⩾2, with initial data and v0∈W1, ∞(Ω) satisfying u0⩾0 and v0>0 in . It is shown that if then for any such data there exists a global-in-time classical solution, generalizing a previous result which asserts the same for n=2 only. Furthermore, it is seen that the range of admissible χ can be enlarged upon relaxing the solution concept. More precisely, global existence of weak solutions is established whenever . Copyright © 2010 John Wiley & Sons, Ltd.

Robust Partial-State Feedback Prescribed Performance Control of Cascade Systems With Unknown Nonlinearities

Abstract: A universal controller is designed for cascade systems, involving dynamic uncertainty, unknown nonlinearities, exogenous disturbances and/or time-varying parameters, capable of guaranteeing prescribed performance for the output tracking error, as well as uniformly bounded signals in the closed loop. By prescribed performance we mean that the output tracking error should converge to a predefined arbitrarily small residual set, with convergence rate no less than a certain prespecified value, exhibiting maximum overshoot less than a sufficiently small preassigned constant. The proposed control scheme is of low complexity, utilizes partial state feedback and requires reduced levels of a priori system knowledge. The results can be easily extended to systems affected by bounded state measurement errors, as well as to MIMO nonlinear systems in block triangular form. Simulations clarify and verify the approach.

Universality of blow-up profile for small radial type II blow-up solutions of the energy-critical wave equation

Abstract: Consider the energy-critical focusing wave equation on the Euclidian space. A blow-up type II solution of this equation is a solution which has finite time of existence but stays bounded in the energy space. The aim of this work is to exhibit universal properties of such solutions. Let W be the unique radial positive stationary solution of the equation. Our main result is that in dimension 3, under an appropriate smallness assumption, any type II blow-up radial solution is essentially the sum of a rescaled W concentrating at the origin and a small remainder which is continuous with respect to the time variable in the energy space. This is coherent with the solutions constructed by Krieger, Schlag and Tataru. One ingredient of our proof is that the unique radial solution which is compact up to scaling is equal to W up to symmetries.

Pseudorandom knapsacks and the sample complexity of LWE search-to-decision reductions

Abstract: We study the pseudorandomness of bounded knapsack functions over arbitrary finite abelian groups. Previous works consider only specific families of finite abelian groups and 0-1 coefficients. The main technical contribution of our work is a new, general theorem that provides sufficient conditions under which pseudorandomness of bounded knapsack functions follows directly from their one-wayness. Our results generalize and substantially extend previous work of Impagliazzo and Naor (J. Cryptology 1996). As an application of the new theorem, we give sample preserving search-to-decision reductions for the Learning With Errors (LWE) problem, introduced by (Regev, STOC 2005) and widely used in lattice-based cryptography. Concretely, we show that, for a wide range of parameters, m LWE samples can be proved indistinguishable from random just under the hypothesis that search LWE is a one-way function for the same number m of samples.

Adaptive Position Tracking of VTOL UAVs

Abstract: An adaptive position-tracking control scheme is proposed for vertical take-off and landing (VTOL) unmanned airborne vehicles (UAVs) for a set of bounded external disturbances. The control design is achieved in three main steps. The first step is devoted to the design of an a priori bounded linear acceleration driving the translational dynamics toward the desired trajectory. In the second step, we extract the required a priori bounded thrust and the desired attitude, in terms of unit quaternion, from the desired acceleration derived in the first step. In the last step, we design the required torque for the rotational dynamics, allowing the system's attitude to be driven toward the desired attitude obtained at the second step. Two control laws for the system control torque are rigorously designed. The first control law ensures that the position-tracking objective is satisfied for any initial conditions, whereas the second ensures that the tracking objective is satisfied for a set of initial conditions, which is dependant on the control gains. The latter case is included, since it is less complicated than the former control law and may be advantageous from a practical point of view. Finally, simulation results are provided to illustrate the effectiveness of the proposed control strategy.

Finite subgroups of algebraic groups

Abstract: Generalizing a classical theorem of Jordan to arbitrary characteristic, we prove that every finite subgroup of GLn over a field of any characteristic p possesses a subgroup of bounded index which is composed of finite simple groups of Lie type in characteristic p, a commutative group of order prime to p, and a p-group. While this statement can be deduced from the classification of finite simple groups, our proof is self-contained and uses methods only from algebraic geometry and the theory of linear algebraic groups. We believe that our results can serve as a viable substitute for classification in a range of applications in various areas of mathematics.

Stable group theory and approximate subgroups

Abstract: We note a parallel between some ideas of stable model theory and certain topics in finite combinatorics related to the sum-product phenomenon. For a simple linear group G, we show that a finite subset X with |X X \^{-1} X |/ |X| bounded is close to a finite subgroup, or else to a subset of a proper algebraic subgroup of G. We also find a connection with Lie groups, and use it to obtain some consequences suggestive of topological nilpotence. Combining these methods with Gromov's proof, we show that a finitely generated group with an approximate subgroup containing any given finite set must be nilpotent-by-finite. Model-theoretically we prove the independence theorem and the stabilizer theorem in a general first-order setting.

H∞ finite-time control for switched nonlinear discrete-time systems with norm-bounded disturbance

Abstract: Finite-time stability concerns the boundness of system during a fixed finite-time interval. For switched systems, finite-time stability property can be affected significantly by switching behavior; however, it was neglected by most previous research. In this paper, the problems of finite-time stability analysis and stabilization for switched nonlinear discrete-time systems are addressed. First, sufficient conditions are given to ensure a class of switched nonlinear discrete-time system subjected to norm bounded disturbance finite-time bounded under arbitrary switching, and then the results are extended to H∞ finite-time boundness of switched nonlinear discrete-time systems. Finally based on the results on finite-time boundness, the state feedback controller is designed to H∞ finite-time stabilize a switched nonlinear discrete-time system. A numerical design example is given to illustrate the proposed results within this paper.

Proof of the fundamental gap conjecture

Abstract: We prove the Fundamental Gap Conjecture, which states that the difference between the first two Dirichlet eigenvalues (the spec- tral gap) of a Schrodinger operator with convex potential and Dirichlet boundary data on a convex domain is bounded below by the spectral gap on an interval of the same diameter with zero potential. More generally, for an arbitrary smooth potential in higher dimensions, our proof gives both a sharp lower bound for the spectral gap and a sharp modulus of concavity for the logarithm of the first eigenfunction, in terms of the diameter of the domain and a modulus of convexity for the potential.

Dynamic rays of bounded-type entire functions

Abstract: We construct an entire function in the Eremenko-Lyubich class B whose Julia set has only bounded path-components. This answers a question of Eremenko from 1989 in the negative. On the other hand, we show that for many functions in B, in particular those of nite order, every escaping point can be connected to 1 by a curve of escaping points. This gives a partial positive answer to the aforementioned question of Eremenko, and answers a question of Fatou from 1926.