Showing papers on "Piecewise linear function published in 2004"

Adaptive Finite Element Methods with convergence rates

Abstract: Adaptive Finite Element Methods for numerically solving elliptic equations are used often in practice. Only recently [12], [17] have these methods been shown to converge. However, this convergence analysis says nothing about the rates of convergence of these methods and therefore does, in principle, not guarantee yet any numerical advantages of adaptive strategies versus non-adaptive strategies. The present paper modifies the adaptive method of Morin, Nochetto, and Siebert [17] for solving the Laplace equation with piecewise linear elements on domains in ℝ2 by adding a coarsening step and proves that this new method has certain optimal convergence rates in the energy norm (which is equivalent to the H1 norm). Namely, it is shown that whenever s>0 and the solution u is such that for each n≥1, it can be approximated to accuracy O(n−s) in the energy norm by a continuous, piecewise linear function on a triangulation with n cells (using complete knowledge of u), then the adaptive algorithm constructs an approximation of the same type with the same asymptotic accuracy while using only information gained during the computational process. Moreover, the number of arithmetic computations in the proposed method is also of order O(n) for each n≥1. The construction and analysis of this adaptive method relies on the theory of nonlinear approximation.

Least squares finite-element solution of a fractional order two-point boundary value problem

Abstract: In this paper, a theoretical framework for the least squares finite-element approximation of a fractional order differential equation is presented. Mapping properties for fractional dimensional operators on suitable fractional dimensional spaces are established. Using these properties existence and uniqueness of the least squares approximation is proven. Optimal error estimates are proven for piecewise linear trial elements. Numerical results are included which confirm the theoretical results.

Piecewise linear fitting and trend changing points of climate parameters

Abstract: [1] Finding an overall linear trend is a common method in scientific studies. It is almost a requirement when one intends to study variability. Nevertheless, when dealing with long climate temporal series, fitting a straight line only seldom has a relevant meaning. This paper proposes and describes a new methodology for finding overall trends, and, simultaneously, for computing a new set of climate parameters: the breakpoints between periods with significantly different trends. The proposed methodology uses a least-squares approach to compute the best continuous set of straight lines that fit a given time series, subject to a number of constraints on the minimum distance between breakpoints and on the minimum trend change at each breakpoint. The method is tested with three climate time series.

A greedy Delaunay-based surface reconstruction algorithm

Abstract: In this paper, we present a new greedy algorithm for surface reconstruction from unorganized point sets. Starting from a seed facet, a piecewise linear surface is grown by adding Delaunay triangles one by one. The most plausible triangles are added first and in such a way as to prevent the appearance of topological singularities. The output is thus guaranteed to be a piecewise linear orientable manifold, possibly with boundary. Experiments show that this method is very fast and achieves topologically correct reconstruction in most cases. Moreover, it can handle surfaces with complex topology, boundaries, and nonuniform sampling.

Penalized triograms: Total variation regularization for bivariate smoothing

Abstract: Summary. Hansen, Kooperberg and Sardy introduced a family of continuous, piecewise linear functions defined over adaptively selected triangulations of the plane as a general approach to statistical modelling of bivariate densities and regression and hazard functions. These triograms enjoy a natural affine equivariance that offers distinct advantages over competing tensor product methods that are more commonly used in statistical applications. Triograms employ basis functions consisting of linear 'tent functions' defined with respect to a triangulation of a given planar domain. As in knot selection for univariate splines, Hansen and colleagues adopted the regression spline approach of Stone. Vertices of the triangulation are introduced or removed sequentially in an effort to balance fidelity to the data and parsimony. We explore a smoothing spline variant of the triogram model based on a roughness penalty adapted to the piecewise linear structure of the triogram model. We show that the roughness penalty proposed may be interpreted as a total variation penalty on the gradient of the fitted function. The methods are illustrated with real and artificial examples, including an application to estimated quantile surfaces of land value in the Chicago metropolitan area.

Multistability of discrete-time recurrent neural networks with unsaturating piecewise linear activation functions

Abstract: This paper studies the multistability of a class of discrete-time recurrent neural networks with unsaturating piecewise linear activation functions. It addresses the nondivergence, global attractivity, and complete stability of the networks. Using the local inhibition, conditions for nondivergence are derived, which not only guarantee nondivergence, but also allow for the existence of multiequilibrium points. Under these nondivergence conditions, global attractive compact sets are obtained. Complete stability is studied via constructing novel energy functions and using the well-known Cauchy Convergence Principle. Examples and simulation results are used to illustrate the theory.

On Test Functions for Evolutionary Multi-objective Optimization

Abstract: In order to evaluate the relative performance of optimization algorithms benchmark problems are frequently used. In the case of multi-objective optimization (MOO), we will show in this paper that most known benchmark problems belong to a constrained class of functions with piecewise linear Pareto fronts in the parameter space. We present a straightforward way to define benchmark problems with an arbitrary Pareto front both in the fitness and parameter spaces. Furthermore, we introduce a difficulty measure based on the mapping of probability density functions from parameter to fitness space. Finally, we evaluate two MOO algorithms for new benchmark problems.

Absolute stability with a generalized sector condition

Abstract: This paper generalizes the linear sector in the classical absolute stability theory to a sector bounded by concave/convex functions. This generalization allows more flexible or more specific description of the nonlinearity and will thus reduce the conservatism in the estimation of the domain of attraction. We introduce the notions of generalized sector and absolute contractive invariance for estimating the domain of attraction of the origin. Necessary and sufficient conditions are identified under which an ellipsoid is absolutely contractively invariant. In the case that the sector is bounded by piecewise linear concave/convex functions, these conditions can be exactly stated as linear matrix inequalities. Moreover, if we have a set of absolutely contractively invariant (ACI) ellipsoids, then their convex hull is also ACI and inside the domain of attraction. We also present optimization technique to maximize the absolutely contractively invariant ellipsoids for the purpose of estimating the domain of attraction. The effectiveness of the proposed method is illustrated with examples.

On test functions for evolutionary multi-objective optimization

Abstract: In order to evaluate the relative performance of optimization algorithms benchmark problems are frequently used. In the case of multi-objective optimization (MOO), we will show in this paper that most known benchmark problems belong to a constrained class of functions with piecewise linear Pareto fronts in the parameter space. We present a straightforward way to define benchmark problems with an arbitrary Pareto front both in the fitness and parameter spaces. Furthermore, we introduce a difficulty measure based on the mapping of probability density functions from parameter to fitness space. Finally, we evaluate two MOO algorithms for new benchmark problems.

Approximation of sigmoid function and the derivative for hardware implementation of artificial neurons

Abstract: A piecewise linear recursive approximation scheme is applied to the computation of the sigmoid function and its derivative in artificial neurons with learning capability. The scheme provides high approximation accuracy with very low memory requirements. The recursive nature of this method allows for the control of the rate accuracy/computation-delay just by modifying one parameter with no impact on the occupied area. The error analysis shows an accuracy comparable to or better than other reported piecewise linear approximation schemes. No multiplier is needed for a digital implementation of the sigmoid generator and only one memory word is required to store the parameter that optimises the approximation.

Dynamic programming for structured continuous Markov decision problems

Abstract: We describe an approach for exploiting structure in Markov Decision Processes with continuous state variables. At each step of the dynamic programming, the state space is dynamically partitioned into regions where the value function is the same throughout the region. We first describe the algorithm for piecewise constant representations. We then extend it to piecewise linear representations, using techniques from POMDPs to represent and reason about linear surfaces efficiently. We show that for complex, structured problems, our approach exploits the natural structure so that optimal solutions can be computed efficiently.

Iso-planar piecewise linear NC tool path generation from discrete measured data points

Abstract: This article presents a method of generating iso-planar piecewise linear NC tool paths for three-axis surface machining using ball-end milling directly from discrete measured data points. Unlike the existing tool path generation methods for discrete points, both the machining error and the machined surface finish are explicitly considered and evaluated in the present work. The primary direction of the generated iso-planar tool paths is derived from the projected boundary of the discrete points. A projected cutter location net (CL-net) is then created, which groups the data points according to the intended machining error and surface finish requirements. The machining error of an individual data point is evaluated within its bounding CL-net cell from the adjacent tool swept surfaces of the ball-end mill. The positions of the CL-net nodes can thus be optimized and established sequentially by minimizing the machining error of each CL-net cell. Since the linear edges of adjacent CL-net cells are in general not perfectly aligned, weighted averages of the associated CL-net nodes are employed as the CL points for machining. As a final step, the redundant segments on the CL paths are trimmed to reduce machining time. The validity of the tool path generation method has been examined by using both simulated and experimentally measured data points.

Frequency Response of a Piecewise Linear Vibration Isolator

Abstract: Piecewise linear vibration isolators are designed to optimally balance the competing goals of motion control and isolation. The piecewise linear system represents a hard nonlinearity, which cannot be assumed small, and hence standard perturbation methods are unable to provide a complete analytical solution. To date there is no frequency response equation reported for piecewise linear isolator systems to include both dual damping and stiffness behavior. In this investigation an averaging method was adopted to explore the frequency response of a symmetric piecewise linear isolator at resonance. The result obtained by an averaging method is in agreement with numerical simulation and experimental measurements. Preliminary sensitivity analysis is conducted to find the effect of system parameters. It appears that the damping ratio plays a more dominant role than stiffness in piecewise linear vibration isolators.

The Ultra-Weak Variational Formulation for Elastic Wave Problems

Abstract: The ultra-weak variational formulation has been used effectively to solve time-harmonic acoustic and electromagnetic wave propagation in inhomogeneous media. We develop the ultra-weak variational formulation for elastic wave propagation in two space dimensions. In order to improve the accuracy and stability of the method, we find it necessary to approximate the S- and P-wave components of the solution in a balanced way. Some preliminary analysis is provided and numerical evidence is presented for the efficiency of the scheme in comparison to piecewise linear finite elements.

Nonlinear Control Allocation Using Piecewise Linear Functions

Abstract: : A novel method is presented for the solution of the non-linear control allocation problem. Historically, control allocation has been performed by assuming that a linear relationship exists between the control induced moments and the control effector displacements. However, aerodynamic databases are discrete valued and almost always stored in multidimensional look up tables where it is assumed that the data is connected by piecewise linear functions. The approach that is presented utilizes the piecewise linear assumption for the control effector moment data. This assumption allows the non-linear control allocation problem to be cast as a piecewise linear program. The piecewise linear program is ultimately cast as a mixed-integer linear program, and it is shown that this formulation solves the control allocation problem exactly. The performance of a re-usable launch vehicle using the piecewise linear control allocation method is shown to be markedly improved when compared to the performance of a more traditional control allocation approach that assumes linearity.

Linear and Cubic Box Splines for the Body Centered Cubic Lattice

Abstract: In this paper we derive piecewise linear and piecewise cubic box spline reconstruction filters for data sampled on the body centered cubic (BCC) lattice. We analytically derive a time domain representation of these reconstruction filters and using the Fourier slice-projection theorem we derive their frequency responses. The quality of these filters, when used in reconstructing BCC sampled volumetric data, is discussed and is demonstrated with a raycaster. Moreover, to demonstrate the superiority of the BCC sampling, the resulting reconstructions are compared with those produced from similar filters applied to data sampled on the Cartesian lattice.

Local and Global Comparison of Continuous Functions

Abstract: We introduce local and global comparison measures for a collection of k /spl les/ d real-valued smooth functions on a common d-dimensional Riemannian manifold. For k = d = 2 we relate the measures to the set of critical points of one function restricted to the level sets of the other. The definition of the measures extends to piecewise linear functions for which they are easy to compute. The computation of the measures forms the centerpiece of a software tool which we use to study scientific datasets.

Application of a dynamic vibration absorber to a piecewise linear beam system

Abstract: This paper deals with the application of a linear dynamic vibration absorber (DVA) to a piecewise linear beam system to suppress its first harmonic resonance. Both the undamped and the damped DVAs are considered. Results of experiments and simulations are presented and show good resemblance. It appears that the undamped DVA is able to suppress the harmonic resonance, while simultaneously many subharmonics appear. The damped DVA suppresses the first harmonic resonance as well as its super- and subharmonics.

A DC piecewise affine model and a bundling technique in nonconvex nonsmooth minimization

Abstract: We introduce an algorithm to minimize a function of several variables with no convexity nor smoothness assumptions. The main peculiarity of our approach is the use of an objective function model which is the difference of two piecewise affine convex functions. Bundling and trust region concepts are embedded into the algorithm. Convergence of the algorithm to a stationary point is proved and some numerical results are reported.

On the Construction and Analysis of High Order Locally Conservative Finite Volume-Type Methods for One-Dimensional Elliptic Problems

Abstract: Locally conservative, finite volume-type methods based on continuous piecewise polynomial functions of degree r \ge 2 are introduced and analyzed in the context of indefinite elliptic problems in one space dimension. The new methods extend and generalize the classical finite volume method based on piecewise linear functions. We derive a priori error estimates in the L2, H1, and L^\infty norm and discuss superconvergence effects for the error and its derivative. Explicit, residual-based a posteriori error bounds in the L2 and energy norm are also derived. We compute the experimental order of convergence and show the results of an adaptive algorithm based on the a posteriori error estimates.

Toward an optimal supervised classifier for the analysis of hyperspectral data

Abstract: In this paper, we propose a supervised classifier based on implementation of the Bayes rule with kernels. The proposed technique first proposes an implicit nonlinear transformation of the data into a feature space seeking to fit normal distributions having a common covariance matrix onto the mapped data. One requirement of this approach is the evaluation of posterior probabilities. We express the discriminant function in dot-product form, and then apply the kernel concept to efficiently evaluate the posterior probabilities. The proposed technique gives the flexibility required to model complex data structures that originate from a wide range of class-conditional distributions. Although we end up with piecewise linear decision boundaries in the feature space, these corresponds to powerful nonlinear boundaries in the original input space. For the data we considered, we have obtained some encouraging results.

Evaluation of the BEASY program using linear and piecewise linear approaches for the boundary conditions

Abstract: The boundary element method (BEM) was used to study galvanic corrosion using linear and logarithmic boundary conditions. The linear boundary condition was implemented by using the linear approach and the piecewise linear approach. The logarithmic boundary condition was implemented by the piecewise linear approach. The calculated potential and current density distribution were compared with the prior analytical results. For the linear boundary condition, the BEASY program using the linear approach and the piecewise linear approach gave accurate predictions of the potential and the galvanic current density distributions for varied electrolyte conditions, various film thicknesses, various electrolyte conductivities and various area ratio of anode/cathode. The 50-point piecewise linear method could be used with both linear and logarithmic polarization curves.

Piecewise quadratic Lyapunov functions for piecewise affine time-delay systems

Abstract: We investigate some particular classes of hybrid systems subject to a class of time delays; the time delays can be constant or time varying. For such systems, we present the corresponding classes of piecewise continuous Lyapunov functions.