Analysis of nonlinear electrical circuits using bernstein polynomials

In electrical circuit analysis, it is often necessary to find the set of all direct current (d.c.) operating points (either voltages or currents) of nonlinear circuits. In general, these nonlinear equations are often represented as polynomial systems. In this paper, we address the problem of finding the solutions of nonlinear electrical circuits, which are modeled as systems of n polynomial equations contained in an n-dimensional box. Branch and Bound algorithms based on interval methods can give guaranteed enclosures for the solution. However, because of repeated evaluations of the function values, these methods tend to become slower. Branch and Bound algorithm based on Bernstein coefficients can be used to solve the systems of polynomial equations. This avoids the repeated evaluation of function values, but maintains more or less the same number of iterations as that of interval branch and bound methods. We propose an algorithm for obtaining the solution of polynomial systems, which includes a pruning step using Bernstein Krawczyk operator and a Bernstein Coefficient Contraction algorithm to obtain Bernstein coefficients of the new domain. We solved three circuit analysis problems using our proposed algorithm. We compared the performance of our proposed algorithm with INTLAB based solver and found that our proposed algorithm is more efficient and fast.     

Extended interval Newton method based on the precise quotient set

The interval Newton method can be used for computing an enclosure of a single simple zero of a smooth function in an interval domain. It can practically be extended to allow computing enclosures of all zeros in a given interval. This paper deals with the extended interval Newton method. An essential operation of the method is division by an interval that contains zero (extended interval division). This operation has been studied by many researchers in recent decades, but inconsistency in the research has occurred again and again. This paper adopts the definition of extended interval division redefined in recent documents (Kulisch in Arithmetic operations for floating-point intervals, 2009; Pryce in P1788: IEEE standard for interval arithmetic version 02.2, 2010). The result of the division is called the precise quotient set. Earlier definitions differ in the overestimation of the quotient set in particular cases, causing inefficiency in Newton’s method and even leading to redundant enclosures of a zero. The paper reviews and compares some extended interval quotient sets defined during the last few decades. As a central theorem, we present the fundamental properties of the extended interval Newton method based on the precise quotient set. On this basis, we develop an algorithm and a convenient program package for the extended interval Newton method. Statements on its convergence are also given. We then demonstrate the performance of the algorithm through nine carefully selected very sensitive numerical examples and show that it can compute correct enclosures of all zeros of the functions with high efficiency, particularly in cases where earlier methods are less effective.     

Symbolic and Numeric Methods for Exploiting Structure in Constructing Resultant Matrices

Resultants characterize the existence of roots of systems of multivariate nonlinear polynomial equations, while their matrices reduce the computation of all common zeros to a problem in linear algebra. Sparse elimination theory has introduced the sparse (or toric) resultant, which takes into account the sparse structure of the polynomials. The construction of sparse resultant, or Newton, matrices is the critical step in the computation of the multivariate resultant and the solution of a nonlinear system. We reveal and exploit the quasi-Toeplitz structure of the Newton matrix, thus decreasing the time complexity of constructing such matrices by roughly one order of magnitude to achieve quasi-quadratic complexity in the matrix dimension. The space complexity is also decreased analogously. These results imply similar improvements in the complexity of computing the resultant polynomial itself and of solving zero-dimensional systems. Our approach relies on fast vector-by-matrix multiplication and uses the following two methods as building blocks. First, a fast and numerically stable method for determining the rank of rectangular matrices, which works exclusively over floating point arithmetic. Second, exact polynomial arithmetic algorithms that improve upon the complexity of polynomial multiplication under our model of sparseness, offering bounds linear in the number of variables and the number of non-zero terms.     

Box-Splitting strategies for the interval Gauss-Seidel step in a global optimization method

We consider an algorithm for computing verified enclosures for all global minimizersx ^* and for the global minimum valuef ^*=f(x ^*) of a twice continuously differentiable functionf:? ⁿ →→ within a box [x]∈I→. Our algorithm incorporates the interval Gauss-Seidel step applied to the problem of finding the zeros of the gradient off. Here, we have to deal with the gaps produced by the extended interval division. It is possible to use different box-splitting strategies for handling these gaps, producing different numbers of subboxes. We present results concerning the impact of these strategies on the interval Gauss-Seidel step and therefore on our global optimization method. First, we give an overview of some of the techniques used in our algorithm, and we describe the modifications improving the efficiency of the interval Gauss-Seidel step by applying a special box-splitting strategy. Then, we have a look on special preconditioners for the Gauss-Seidel step, and we investigate the corresponding results for different splitting strategies. Test results for standard global optimization problems are discussed for different variants of our method in its portable PASCAL-XSC implementation. These results demonstrate that there are many cases in which the splitting strategy is more important for the efficiency of the algorithm than the use of preconditioners.     

On a second order method for the simultaneous inclusion of polynomial complex zeros in rectangular arithmetic

Starting from separated rectangles in the complex plane which contain polynomial complex zeros, an iterative method of second order for the simultaneous inclusion of these zeros is formulated in rectangular arithmetic. The convergence and a condition for convergence are considered. Applying Gauss-Seidel approach to the proposed method, two accelerated interval methods are formulated. TheR-order of convergence of these methods is determined. An analysis of the convergence order is given in the presence of rounding errors. The presented methods are illustrated numerically in examples of polynomial equations.     

The Convex Hull of Rational Plane Curves

We present an algorithm that computes the convex hull of multiple rational curves in the plane. The problem is reformulated as one of finding the zero-sets of polynomial equations in one or two variables; using these zero-sets we characterize curve segments that belong to the boundary of the convex hull. We also present a preprocessing step that can eliminate many redundant curve segments.     

On polynomial matrix spectral factorization by symmetric extraction

We revise the 1963 Davis algorithm [2] for the spectral factorization of a para-Hermitian nonnegative polynomial matrixPhi, by symmetric factor extraction: this algorithm is careless about zeros at infinity. By introducing the notion of diagonal reducedness ofPhi, we obtain an easy sufficient test for the absence of zeros at infinity. We show then how to getPhi, diagonally reduced by diagonal excess reduction steps (similar to the Oono and Yasuura steps), removing all zeros at infinity, and then how to remove synunetrically finite zeros while keeping el, diagonally reduced (hence, free of zeros at infinity). This results in a revised symmetric extraction spectral factorization algorithm with monotone degree control. An example shows the didactical conceptual simplicity of the method. Appropriate symmetric extraction is discovered by revising and discovering important particular one-sided factor extraction properties of polynomial matrices.     

Ray tracing general parametric surfaces using interval arithmetic

This paper describes an algorithm for ray tracing general parametric surfaces. After dividing the surface adaptively into small parts, a binary tree of these parts is built. For each part a bounding volume is calculated with interval arithmetic. From linear approximations and intervals for the partial derivatives it is possible to construct parallelepipds that adapt the orientation and shape of the surface parts very well and form very tight enclosures. Therefore we can develop an algorithm for rendering that is similar to that used with Bèzier and B-spline surfaces, where the bounding volumes are derived from the convex hull property. The tree of enclosures (generated once in a preprocessing step) guarantees that each ray that hits the surface leads to an iteration on a very small surface part; this iteration can be robustly (and very quickly) performed in real arithmetic.     

Solving nonlinear polynomial systems in the barycentric Bernstein basis

We present a method for solving arbitrary systems of N nonlinear polynomials in n variables over an n-dimensional simplicial domain based on polynomial representation in the barycentric Bernstein basis and subdivision. The roots are approximated to arbitrary precision by iteratively constructing a series of smaller bounding simplices. We use geometric subdivision to isolate multiple roots within a simplex. An algorithm implementing this method in rounded interval arithmetic is described and analyzed. We find that when the total order of polynomials is close to the maximum order of each variable, an iteration of this solver algorithm is asymptotically more efficient than the corresponding step in a similar algorithm which relies on polynomial representation in the tensor product Bernstein basis. We also discuss various implementation issues and identify topics for further study.     

A matrix nullspace approach for solving equality-constrained multivariable polynomial least-squares problems

We present an elimination theory-based method for solving equality-constrained multivariable polynomial least-squares problems in system identification. While most algorithms in elimination theory rely upon Groebner bases and symbolic multivariable polynomial division algorithms, we present an algorithm which is based on computing the nullspace of a large sparse matrix and the zeros of a scalar, univariate polynomial.     

参数曲线曲面实奇异点的计算

本文主要讨论了利用Grobner基理论对参数曲线（面）的奇异点进行判断和计算。如果曲线（面）存在奇异点,由定义可知它的导矢（法矢）等于0。因此,曲线（面）奇异点的判定就是方程组的求解问题。由Hilbert弱零点定理可知,若一组多项式方程无公共零点,则其生成理想约化的Grobner基为[1]。在计算时,首先根据Grobner基理论判断 曲线（面）是否存在奇异点。当存在奇异点时,利用区间算法对实奇异点进行隔离和迭代。在确定奇异点的存在性时,根据曲线（曲面）的导矢（法矢）方程的Grobner基直 接进行判断,而不需要求解非线性代数方程组。若曲线曲面存在奇异点,进一步采用区间方法对奇异点进行隔离以确定曲线段或曲面片的正则性。该方法可以得到参数曲线曲面的所有实奇异点且达到任意精度。     

Root-finding by expansion with independent constraints

Elimination methods are highly effective for the solution of linear and nonlinear systems of equations, but reversal of the elimination principle can be beneficial as well: competent incorporation of additional independent constraints and variables or more generally immersion of the original computational problem into a larger task, defined by a larger number of independent constraints and variables can improve global convergence of iterative algorithms, that is their convergence from the start. A well known example is the dual linear and nonlinear programming, which enhances the power of optimization algorithms. We believe that this is just an ad hoc application of general Principle of Expansion with Independent Constraints; it should be explored systematically for devising iterative algorithms for the solution of equations and systems of equations and for optimization. At the end of this paper we comment on other applications and extensions of this principle.Presently we show it at work for the approximation of a single zero of a univariate polynomial p of a degree n. Empirical global convergence of the known algorithms for this task is much weaker than that of the algorithms for all n zeros, such as Weierstrass–Durand–Kerner’s root-finder, which reduces its root-finding task to Viète’s (Vieta’s) system of n polynomial equations with n unknowns. We adjust this root-finder to the approximation of a single zero of p, preserve its fast global convergence and decrease the number of arithmetic operations per iteration from quadratic to linear. Together with computing a zero of a polynomial p, the algorithm deflates this polynomial as by-product, and then could be reapplied to the quotient to approximate the next zero of p. Alternatively by using m processors that exchange no data, one can concurrently approximate up to m zeros of p. Our tests confirm the efficiency of the proposed algorithms.Technically our root-finding boils down to computations with structured matrices, polynomials and partial fraction decompositions. Our study of these links can be of independent interest; e.g., as by-product we express the inverse of a Sylvester matrix via its last column, thus extending the celebrated result of Gohberg and Sementsul (1972) [22] from Toeplitz to Sylvester matrix inverses.     

Bounds for an interval polynomial

We discuss the evaluation of the range of values of an interval polynomial over an interval. Several algorithms are proposed and tested on numerical examples. The algorithms are based on ideas by Cargo and Shiska [2] and Rivlin [4]. The one basic algorithm uses Bernstein polynomials. It is shown to converge to the exact bounds and it has furthermore the property that if the maximum respectively the minimum of the polynomials occurs at an endpoint of the interval then the bound is exact. This is a useful property in routines for polynomials zeros. The other basic method is based on the meanvalue theorem and it has the advantage that the degree of approximation required for a certain apriori tolerance is smaller than the degree required in the Bernstein polynomial case. The mean value method is shown to be at least quadratically convergent and the Bernstein polynomial method is shown to be at least linearly convergent.     

Symbolic Preconditioning with Taylor Models: Some Examples

Deterministic global optimization with interval analysis involves using interval enclosures for ranges of the constraints, objective, and gradient to reject infeasible regions, regions without global optima, and regions without critical points; using interval Newton methods to converge on optimum-containing regions and to verify global optima.There are certain problems for which interval dependency leads to overestimation in the enclosures of the individual components, causing the optimization search to become prohibitively inefficient. As Hansen has observed earlier, in other problems, there is no overestimation in the individual components, but overestimation is introduced in the preconditioning in the interval Newton method.We examine these issues for a particular nonlinear systems problem that, to date, has defied numerical solution. To reduce overestimation, we use Taylor models. The Taylor models sometimes reduce individual overestimation but, consistent with Hansen's observations, especially reduce the overestimation due to preconditioning. From numerical experiments, we conclude that, in certain instances, Taylor models can greatly reduce both the number of subregions necessary to complete an exhaustive search and the total computational effort.     

Robust output feedback control of polynomial growth nonlinear systems with measurement uncertainty

Even in the presence of uncertainty in both state and output equations, we prove that global asymptotic stabilization is still possible by output feedback for a family of uncertain nonlinear systems dominated by a triangular system with a polynomial output‐dependent growth rate. In contrast to the linear growth requirement in the recent work the nonlinear perturbations in this paper are allowed to satisfy a linear growth condition with a polynomial output‐dependent rate. To handle simultaneously the polynomial nonlinearities and unknown parameter in the system output, we propose a high‐gain estimator with a dynamic gain that is updated online through a Riccati‐type dynamic equation. Then, an estimator‐based controller is designed by a recursive algorithm that makes it possible to assign the controller gains step by step. The globally stabilizing output‐feedback controller developed in this paper is robust with respect to uncertainties in the system dynamics and output equations.     

基于GIS的零售业商圈分析

科学、合理的市场需求分析和选址分析是零售商投资决策的重要依据。介绍了零售业商圈分析的基本过程及相应分析方法,并利用组件GIS技术,在商业地理信息管理系统的基础上构建了市场饱和度分析、商圈划分、需求估计3个商业应用分析模型,实现了对商圈的定量及可视化分析。最后以上海市徐汇区为研究区域,以大型综合超市这一商业业态为例进行了实验分析。     

飞行器上升段轨迹的优化设计

针对飞行器上升段轨迹优化求解困难的问题,提出一种基于正交配点的优化求解方法。该方法以第二类切比雪夫正交多项式的零点作为系统控制变量和状态变量的离散点,利用拉格朗日插值多项式对状态和控制变量进行拟合。通过对多项式的求导将动力学微分方程约束转化为代数约束,从而把无限维的最优控制问题转化为一个有限维的非线性规划(Nonlinear Programming,NLP)问题。随后,利用序列二次规划(Sequential Quadratic Program-ming,SQP)方法求解转化后的NLP问题,获得最优的飞行轨迹。最后,飞行器上的仿真结果验证了所提方法的有效性。研究成果可为飞行器的制导控制提供可行的飞行轨迹,有一定的工程应用价值。     

A Toeplitz algorithm for polynomial J-spectral factorization

A block Toeplitz algorithm is proposed to perform the J-spectral factorization of a para-Hermitian polynomial matrix. The input matrix can be singular or indefinite, and it can have zeros along the imaginary axis. The key assumption is that the finite zeros of the input polynomial matrix are given as input data. The algorithm is based on numerically reliable operations only, namely computation of the null-spaces of related block Toeplitz matrices, polynomial matrix factor extraction and linear polynomial matrix equations solving.     

Zeros of equivariant vector fields: Algorithms for an invariant approach

We present a symbolic algorithm to solve for the zeros of a polynomial vector field equivariant with respect to a finite subgroup of O (n). We prove that the module of equivariant. polynomial maps for a finite matrix group is Cohen-Macaulay and give an algorithm to compute a fundamental basis. Equivariant normal forms are easily computed from this basis. We use this basis to transform the problem of finding the zeros of an equivariant map to the problem of finding zeros of a set of invariant polynomials. Solving for the values of fundamental polynomial invariants at the zeros effectively reduces each group orbit of solutions to a single point. Our emphasis is on a computationally effective algorithm and we present our techniques applied to two examples.     

Calculating polynomial zeros on a local memory parallel computer

We consider the calculation, on a local memory parallel computer, of all the zeros of an n th degree polynomial P_n(x) which has real coefficients. We describe a generic parallel algorith, which approximates all the zeros simultaneously and we give three specific examples of this algorithm which have orders of convergence two, three and four. We report extensive numerical tests of the algorithms; the fourth order algorithm is not robust, with many failures to convergence, whereas the other two algorithms are reliable and display very respectable parallel speedups for higher degree polynomials.