Showing papers on "Piecewise linear function published in 2012"

Solving Inverse Problems With Piecewise Linear Estimators: From Gaussian Mixture Models to Structured Sparsity

Abstract: A general framework for solving image inverse problems with piecewise linear estimations is introduced in this paper. The approach is based on Gaussian mixture models, which are estimated via a maximum a posteriori expectation-maximization algorithm. A dual mathematical interpretation of the proposed framework with a structured sparse estimation is described, which shows that the resulting piecewise linear estimate stabilizes the estimation when compared with traditional sparse inverse problem techniques. We demonstrate that, in a number of image inverse problems, including interpolation, zooming, and deblurring of narrow kernels, the same simple and computationally efficient algorithm yields results in the same ballpark as that of the state of the art.

A Mixed-Integer Linear Programming Approach for Multi-Stage Security-Constrained Transmission Expansion Planning

Abstract: The transmission expansion planning (TEP) problem in modern power systems is a large-scale, mixed-integer, non-linear and non-convex problem. Although remarkable advances have been made in optimization techniques, finding an optimal solution to a problem of this nature can still be extremely challenging. Based on the linearized power flow model, this paper presents a mixed-integer linear programming (MILP) approach that considers losses, generator costs and the N - 1 security constraints for the multi-stage TEP problem. The losses and generator cost are modeled as piecewise linear functions of the line flows and the generator outputs, respectively. The IEEE 24-bus system is used to compare the lossy and the lossless model. The results show that the lossy model provides savings in total cost in the long run. The selection of the best number of piecewise linear sections L is also shown. Then a complete planning framework is presented and a multi-stage TEP is performed on the IEEE 118-bus test system. Simulation results show that the proposed approach is accurate and efficient, and has the potential to be applied to large-scale power system planning problems.

Bounded distortion mapping spaces for triangular meshes

Abstract: The problem of mapping triangular meshes into the plane is fundamental in geometric modeling, where planar deformations and surface parameterizations are two prominent examples. Current methods for triangular mesh mappings cannot, in general, control the worst case distortion of all triangles nor guarantee injectivity.This paper introduces a constructive definition of generic convex spaces of piecewise linear mappings with guarantees on the maximal conformal distortion, as-well as local and global injectivity of their maps. It is shown how common geometric processing objective functionals can be restricted to these new spaces, rather than to the entire space of piecewise linear mappings, to provide a bounded distortion version of popular algorithms.

Canonical Discontinuous Planar Piecewise Linear Systems

Abstract: The family of Filippov systems constituted by planar discontinuous piecewise linear systems with two half-plane linearity zones is considered. Under generic conditions that amount to the boundedness of the sliding set, some changes of variables and parameters are used to obtain a Lienard-like canonical form with seven parameters. This canonical form is topologically equivalent to the original system if one restricts one's attention to orbits with no points in the sliding set. Under the assumption of focus-focus dynamics, a reduced canonical form with only five parameters is obtained. For the case without equilibria in both open half-planes we describe the qualitatively different phase portraits that can occur in the parameter space and the bifurcations connecting them. In particular, we show the possible existence of two limit cycles surrounding the sliding set. Such limit cycles bifurcate at certain parameter curves, organized around different codimension-two Hopf bifurcation points. The proposed canonic...

Cross-based local multipoint filtering

Abstract: This paper presents a cross-based framework of performing local multipoint filtering efficiently. We formulate the filtering process as a local multipoint regression problem, consisting of two main steps: 1) multipoint estimation, calculating the estimates for a set of points within a shape-adaptive local support, and 2) aggregation, fusing a number of multipoint estimates available for each point. Compared with the guided filter that applies the linear regression to all pixels covered by a fixed-sized square window non-adaptively, the proposed filtering framework is a more generalized form. Two specific filtering methods are instantiated from this framework, based on piecewise constant and piecewise linear modeling, respectively. Leveraging a cross-based local support representation and integration technique, the proposed filtering methods achieve theoretically strong results in an efficient manner, with the two main steps' complexity independent of the filtering kernel size. We demonstrate the strength of the proposed filters in various applications including stereo matching, depth map enhancement, edge-preserving smoothing, color image denoising, detail enhancement, and flash/no-flash denoising.

On the number of limit cycles in general planar piecewise linear systems

Abstract: Much progress has been made in planar piecewise smooth dynamical systems. However there remain many important problems to be solved even in planar piecewise linear systems. In this paper, we investigate the number of limit cycles of planar piecewise linear systems with two linear regions sharing the same equilibrium. By studying the implicit Poincare map induced by the discontinuity boundary, some cases when there exist at most 2 limit cycles is completely investigated. Especially, based on these results we provide an example along with numerical simulations to illustrate the existence of 3 limit cycles thus have a negative answer to the conjecture by M. Han and W. Zhang [11](J. Differ.Equations 248 (2010) 2399-2416) that piecewise linear systems with only two regions have at most 2 limit cycles.

A road to classification in high dimensional space: the regularized optimal affine discriminant

Abstract: For high-dimensional classification, it is well known that naively performing the Fisher discriminant rule leads to poor results due to diverging spectra and noise accumulation. Therefore, researchers proposed independence rules to circumvent the diverging spectra, and sparse independence rules to mitigate the issue of noise accumulation. However, in biological applications, there are often a group of correlated genes responsible for clinical outcomes, and the use of the covariance information can significantly reduce misclassification rates. In theory the extent of such error rate reductions is unveiled by comparing the misclassification rates of the Fisher discriminant rule and the independence rule. To materialize the gain based on finite samples, a Regularized Optimal Affine Discriminant (ROAD) is proposed. ROAD selects an increasing number of features as the regularization relaxes. Further benefits can be achieved when a screening method is employed to narrow the feature pool before hitting the ROAD. An efficient Constrained Coordinate Descent algorithm (CCD) is also developed to solve the associated optimization problems. Sampling properties of oracle type are established. Simulation studies and real data analysis support our theoretical results and demonstrate the advantages of the new classification procedure under a variety of correlation structures. A delicate result on continuous piecewise linear solution path for the ROAD optimization problem at the population level justifies the linear interpolation of the CCD algorithm.

Adaptive Control of Piecewise Linear Systems: the State Tracking Case

Abstract: Nonlinear controlled systems at multiple operating points are modeled as piecewise linear systems, where changes in operating points are modeled as switches between constituent linearized systems. This note studies the adaptive state feedback for state tracking control problem for such systems. Piecewise linear reference model systems are used for generating desired state trajectories and their stability properties are studied. Adaptive state feedback control schemes are developed, and their stability and tracking performance are analyzed and evaluated by simulation examples. It is shown that exponential tracking performance can be achieved if the reference input is sufficiently rich and the switches are sufficiently slow.

Stabilizing Model Predictive Control of Stochastic Constrained Linear Systems

Abstract: This paper investigates stochastic stabilization procedures based on quadratic and piecewise linear Lyapunov functions for discrete-time linear systems affected by multiplicative disturbances and subject to linear constraints on inputs and states. A stochastic model predictive control (SMPC) design approach is proposed to optimize closed-loop performance while enforcing constraints. Conditions for stochastic convergence and robust constraints fulfillment of the closed-loop system are enforced by solving linear matrix inequality problems off line. Performance is optimized on line using multistage stochastic optimization based on enumeration of scenarios, that amounts to solving a quadratic program subject to either quadratic or linear constraints. In the latter case, an explicit form is computable to ease the implementation of the proposed SMPC law. The approach can deal with a very general class of stochastic disturbance processes with discrete probability distribution. The effectiveness of the proposed SMPC formulation is shown on a numerical example and compared to traditional MPC schemes.

Using Piecewise Linear Functions for Solving MINLP s

Abstract: In this chapter we want to demonstrate that in certain cases general mixed integer nonlinear programs (MINLPs) can be solved by just applying purely techniques from the mixed integer linear world. The way to achieve this is to approximate the nonlinearities by piecewise linear functions. The advantage of applying mixed integer lin- ear techniques are that these methods are nowadays very mature, that is, they are fast, robust, and are able to solve problems with up to millions of variables. In addition, these methods have the potential of finding globally optimal solutions or at least to provide solution guarantees. On the other hand, one tends to say at this point “If you have a hammer, everything is a nail.”[15], because one tries to reformulate or to approximate an ac- tual nonlinear problem until one obtains a model that is tractable by the methods one is common with. Besides the fact that this is a very typical approach in mathematics the question stays whether this is a reasonable approach for the solution of MINLPs or whether the nature of the nonlin- earities inherent to the problem gets lost and the solutions obtained from the mixed integer linear problem have no meaning for the MINLP. The purpose of this chapter is to discuss this question. We will see that the truth lies somewhere in between and that there are problems where this is indeed a reasonable way to go and others where it is not.

Biologically Inspired Spiking Neurons : Piecewise Linear Models and Digital Implementation

Abstract: There has been a strong push recently to examine biological scale simulations of neuromorphic algorithms to achieve stronger inference capabilities. This paper presents a set of piecewise linear spiking neuron models, which can reproduce different behaviors, similar to the biological neuron, both for a single neuron as well as a network of neurons. The proposed models are investigated, in terms of digital implementation feasibility and costs, targeting large scale hardware implementation. Hardware synthesis and physical implementations on FPGA show that the proposed models can produce precise neural behaviors with higher performance and considerably lower implementation costs compared with the original model. Accordingly, a compact structure of the models which can be trained with supervised and unsupervised learning algorithms has been developed. Using this structure and based on a spike rate coding, a character recognition case study has been implemented and tested.

Three nested limit cycles in discontinuous piecewise linear differential systems with two zones

Abstract: In this paper we study a planar piecewise linear differential system formed by two regions separated by a straight line so that one system has a real unstable focus and the other a virtual stable focus which coincides with the real one. This system was intro- duced in a very recent paper (On the number of limit cycles in general planar piecewise linear systems, Discrete and Continuous Dynamical Systems-A 32, 2012, pp. 2147-2164) by S.-M. Huan and X.-S. Yang, who numerically showed that it can exhibit 3 limit cycles surrounding the real focus. This is the first example that a non-smooth piecewise linear differential system with two zones can have 3 nested limit cycles of crossing type surround- ing a unique equilibrium. We provide a rigorous computer assisted proof of the quoted numerical result.

Optimality Conditions and Error Analysis of Semilinear Elliptic Control Problems with $L^1$ Cost Functional

Abstract: Semilinear elliptic optimal control problems involving the $L^1$ norm of the control in the objective are considered. Necessary and sufficient second-order optimality conditions are derived. A priori finite element error estimates for piecewise constant discretizations for the control and piecewise linear discretizations of the state are shown. Error estimates for the variational discretization of the problem in the sense of [M. Hinze, Comput. Optim. Appl., 30 (2005), pp. 45--61] are also obtained. Numerical experiments confirm the convergence rates.

The embedding capacity of 4-dimensional symplectic ellipsoids

Abstract: This paper calculates the function c(a) whose value at a is the inmum of the size of a ball that contains a symplectic image of the ellipsoidE(1;a). (Here a 1 is the ratio of the area of the large axis to that of the smaller axis.) The structure of the graph of c(a) is surprisingly rich. The volume constraint implies that c(a) is always greater than or equal to the square root of a, and it is not hard to see that this is equality for large a. However, for a less than the fourth power 4 of the golden ratio, c(a) is piecewise linear, with graph that alternately lies on a line through the origin and is horizontal. We prove this by showing that there are exceptional curves in blow ups of the complex projective plane whose homology classes are given by the continued fraction expansions of ratios of Fibonacci numbers. On the interval [ 4 ; 7] we nd c(a) = (a + 1)=3. For a 7, the function c(a) coincides with the square root except on a nite number of intervals where it is again piecewise linear. The embedding constraints coming from embedded contact homology give rise to another capacity function cECH which may be computed by counting lattice points in appropriate right angled triangles. According to Hutchings and Taubes, the functorial properties of embedded contact homology imply that cECH(a) c(a) for all a. We show here that cECH(a) c(a) for all a.

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.

Biologically Inspired Spiking Neurons: Piecewise Linear Models and Digital Implementation

Abstract: There has been a strong push recently to examine biological scale simulations of neuromorphic algorithms to achieve stronger inference capabilities. This paper presents a set of piecewise linear spiking neuron models, which can reproduce different behaviors, similar to the biological neuron, both for a single neuron as well as a network of neurons. The proposed models are investigated, in terms of digital implementation feasibility and costs, targeting large scale hardware implementation. Hardware synthesis and physical implementations on FPGA show that the proposed models can produce precise neural behaviors with higher performance and considerably lower implementation costs compared with the original model. Accordingly, a compact structure of the models which can be trained with supervised and unsupervised learning algorithms has been developed. Using this structure and based on a spike rate coding, a character recognition case study has been implemented and tested.

Complexity Analysis of the Lasso Regularization Path

Abstract: The regularization path of the Lasso can be shown to be piecewise linear, making it possible to "follow" and explicitly compute the entire path. We analyze in this paper this popular strategy, and prove that its worst case complexity is exponential in the number of variables. We then oppose this pessimistic result to an (optimistic) approximate analysis: We show that an approximate path with at most O(1/√e) linear segments can always be obtained, where every point on the path is guaranteed to be optimal up to a relative e-duality gap. We complete our theoretical analysis with a practical algorithm to compute these approximate paths.

Studies in Astronomical Time Series Analysis. VI. Bayesian Block Representations

Abstract: This paper addresses the problem of detecting and characterizing local variability in time series and other forms of sequential data. The goal is to identify and characterize statistically significant variations, at the same time suppressing the inevitable corrupting observational errors. We present a simple nonparametric modeling technique and an algorithm implementing it - an improved and generalized version of Bayesian Blocks (Scargle 1998) - that finds the optimal segmentation of the data in the observation interval. The structure of the algorithm allows it to be used in either a real-time trigger mode, or a retrospective mode. Maximum likelihood or marginal posterior functions to measure model fitness are presented for events, binned counts, and measurements at arbitrary times with known error distributions. Problems addressed include those connected with data gaps, variable exposure, extension to piecewise linear and piecewise exponential representations, multi-variate time series data, analysis of variance, data on the circle, other data modes, and dispersed data. Simulations provide evidence that the detection efficiency for weak signals is close to a theoretical asymptotic limit derived by (Arias-Castro, Donoho and Huo 2003). In the spirit of Reproducible Research (Donoho et al. 2008) all of the code and data necessary to reproduce all of the figures in this paper are included as auxiliary material.

An active strain electromechanical model for cardiac tissue

Abstract: We propose a finite element approximation of a system of partial differential equations describing the coupling between the propagation of electrical potential and large deformations of the cardiac tissue. The underlying mathematical model is based on the active strain assumption, in which it is assumed that a multiplicative decomposition of the deformation tensor into a passive and active part holds, the latter carrying the information of the electrical potential propagation and anisotropy of the cardiac tissue into the equations of either incompressible or compressible nonlinear elasticity, governing the mechanical response of the biological material. In addition, by changing from an Eulerian to a Lagrangian configuration, the bidomain or monodomain equations modeling the evolution of the electrical propagation exhibit a nonlinear diffusion term. Piecewise quadratic finite elements are employed to approximate the displacements field, whereas for pressure, electrical potentials and ionic variables are approximated by piecewise linear elements. Various numerical tests performed with a parallel finite element code illustrate that the proposed model can capture some important features of the electromechanical coupling, and show that our numerical scheme is efficient and accurate.

Bifurcation of limit cycles by perturbing a piecewise linear Hamiltonian system with a homoclinic loop

Abstract: In this paper, we study limit cycle bifurcations for a kind of non-smooth polynomial differential systems by perturbing a piecewise linear Hamiltonian system with the center at the origin and a homoclinic loop around the origin. By using the first Melnikov function of piecewise near-Hamiltonian systems, we give lower bounds of the maximal number of limit cycles in Hopf and homoclinic bifurcations, and derive an upper bound of the number of limit cycles that bifurcate from the periodic annulus between the center and the homoclinic loop up to the first order in e . In the case when the degree of perturbing terms is low, we obtain a precise result on the number of zeros of the first Melnikov function.

On linear programs with linear complementarity constraints

Abstract: The paper is a manifestation of the fundamental importance of the linear program with linear complementarity constraints (LPCC) in disjunctive and hierarchical programming as well as in some novel paradigms of mathematical programming. In addition to providing a unified framework for bilevel and inverse linear optimization, nonconvex piecewise linear programming, indefinite quadratic programs, quantile minimization, and ? 0 minimization, the LPCC provides a gateway to a mathematical program with equilibrium constraints, which itself is an important class of constrained optimization problems that has broad applications. We describe several approaches for the global resolution of the LPCC, including a logical Benders approach that can be applied to problems that may be infeasible or unbounded.

Synchronization of PWL function-based 2D and 3D multi-scroll chaotic systems

Abstract: We study the synchronization of a piecewise linear function-based chaotic system. That system generates multiple scrolls in multiple directions (two- and three-directions) on phase space. In this scenario, the design of a controller based on Generalized Hamiltonian forms is possible. As function of control signals, we propose a master–slave synchronization scheme using 2 n −1 combinations to drive a nonlinear state observer. Associated with this, the piecewise linear functions of the slave are directly controlled by the state-variables of the master system. We computed the synchronization error for each combinations. Besides, the circuit synthesis based on operational amplifiers validates our synchronization scheme by means of SPICE simulations. We observed that the synchronization error at circuit level depends on the number of the control signals used. Our numerical and SPICE simulation results are in agreement showing the usefulness of the proposed approach.

Control Of Nonlinear Dynamical Systems: Methods And Applications

Abstract: Optimal control.- Method of decomposition (the first approach).- Method of decomposition (the second approach).- Stability based control for Lagrangian mechanical systems.- Piecewise linear control for mechanical systems under uncertainty.- Continuous feedback control for mechanical systems under uncertainty.- Control in distributed-parameter systems.- Control system under complex constraints.- Optimal control problems under complex constraints.- Time-optimal swing-up and damping feedback controls of a nonlinear pendulum.

Topology-adaptive interface tracking using the deformable simplicial complex

Abstract: We present a novel, topology-adaptive method for deformable interface tracking, called the Deformable Simplicial Complex (DSC). In the DSC method, the interface is represented explicitly as a piecewise linear curve (in 2D) or surface (in 3D) which is a part of a discretization (triangulation/tetrahedralization) of the space, such that the interface can be retrieved as a set of faces separating triangles/tetrahedra marked as inside from the ones marked as outside (so it is also given implicitly). This representation allows robust topological adaptivity and, thanks to the explicit representation of the interface, it suffers only slightly from numerical diffusion. Furthermore, the use of an unstructured grid yields robust adaptive resolution. Also, topology control is simple in this setting. We present the strengths of the method in several examples: simple geometric flows, fluid simulation, point cloud reconstruction, and cut locus construction.

Elastic net for cox's proportional hazards model with a solution path algorithm

Abstract: For least squares regression, Efron et al. (2004) proposed an efficient solution path algorithm, the least angle regression (LAR). They showed that a slight modification of the LAR leads to the whole LASSO solution path. Both the LAR and LASSO solution paths are piecewise linear. Recently Wu (2011) extended the LAR to generalized linear models and the quasi-likelihood method. In this work we extend the LAR further to handle Cox's proportional hazards model. The goal is to develop a solution path algorithm for the elastic net penalty (Zou and Hastie (2005)) in Cox's proportional hazards model. This goal is achieved in two steps. First we extend the LAR to optimizing the log partial likelihood plus a fixed small ridge term. Then we define a path modification, which leads to the solution path of the elastic net regularized log partial likelihood. Our solution path is exact and piecewise determined by ordinary differential equation systems.

Complexity Analysis of the Lasso Regularization Path

Abstract: The regularization path of the Lasso can be shown to be piecewise linear, making it possible to "follow" and explicitly compute the entire path. We analyze in this paper this popular strategy, and prove that its worst case complexity is exponential in the number of variables. We then oppose this pessimistic result to an (optimistic) approximate analysis: We show that an approximate path with at most O(1/sqrt(epsilon)) linear segments can always be obtained, where every point on the path is guaranteed to be optimal up to a relative epsilon-duality gap. We complete our theoretical analysis with a practical algorithm to compute these approximate paths.