Error Estimation of Numerical Solvers for Linear Ordinary Differential Equations

Solving Linear Ordinary Differential Equations (ODEs) plays an important role in many applications. There are various numerical methods and solvers to obtain approximate solutions. However, few work about global error estimation can be found in the literature. In this paper, we first give a definition of the residual, based on the piecewise Hermit interpolation, which is a kind of the backward-error of ODE solvers. It indicates the reliability and quality of numerical solution. Secondly, the global error between the exact solution and an approximate solution is the forward error and a bound of it can be given by using the backward-error. The examples in the paper show that our estimate works well for a large class of ODE models.Comment: 13 pages,6 figure

Exact Bivariate Polynomial Factorization in Q by Approximation of Roots

Factorization of polynomials is one of the foundations of symbolic computation. Its applications arise in numerous branches of mathematics and other sciences. However, the present advanced programming languages such as C++ and J++, do not support symbolic computation directly. Hence, it leads to difficulties in applying factorization in engineering fields. In this paper, we present an algorithm which use numerical method to obtain exact factors of a bivariate polynomial with rational coefficients. Our method can be directly implemented in efficient programming language such C++ together with the GNU Multiple-Precision Library. In addition, the numerical computation part often only requires double precision and is easily parallelizable

A Short Note on Zero-error Computation for Algebraic Numbers by IPSLQ

The PSLQ algorithm is one of the most popular algorithm for finding nontrivial integer relations for several real numbers. In the present work, we present an incremental version of PSLQ. For some applications needing to call PSLQ many times, such as finding the minimal polynomial of an algebraic number without knowing the degree, the incremental PSLQ algorithm is more efficient than PSLQ, both theoretically and practically.Comment: 4 page

Geometric involutive bases for positive dimensional polynomial ideals and SDP methods

Geometric involutive bases for polynomial systems of equations have their origin in the prolongation and projection methods of the geometers Cartan and Kuranishi for systems of PDE. They are useful for numerical ideal membership testing and the solution of polynomial systems. In this paper we further develop our symbolic-numeric methods for such bases. We give methods to explicitly extract and decrease the degree of intermediate systems and the output basis. Algorithms for the numerical computation of involutivity criteria for positive dimensional ideals are also discussed. We were also motivated by some remarkable recent work by Lasserre and collaborators who employed our prolongation projection involutive criteria as a part of their semi-definite based programming (SDP) method for identifying the real radical of zero dimensional polynomial ideals. Consequently in this paper we begin an exploration of the interaction between geometric involutive bases and these methods particularly in the positive dimensional case. Motivated by the extension of these methods to the positive dimensional case we explore the interplay between geometric involutive bases and the new SDP methods

Design optimization of mode-matched bulk-mode piezoelectric micro-gyroscopes through modal analysis

Bulk piezoelectric micro-gyroscope is a miniaturized inertial sensor that uses a differential thickness-shear bulk mode of a PZT block as the drive mode of the gyroscope. In the paper, a second differential thickness-extensional mode is identified for the sense mode and mode-matching is proposed for the first time by proper design of the device geomtries. Through finite element modal analysis, the frequencies of drive mode and sense mode are obtained when the length of the PZT block varies from 4.8mm to 5.6mm and the width of the PZT block varies from 3.0mm to 4.0mm. Using a fitting method, the empirical formulae with an excellent fit are induced to predict the influence of the length and the width of the PZT block on the drive and sense mode frequencies. Based on these empirical formulae, the mode-matching equations are introduced. The analysis results show that for a given thickness of the PZT block, the effect of the width on the drive mode frequency is prominant. Conversly, the effect of length on the sense mode frequency is dominant. The resonance frequencies, kinetic energy ratios, scale factors of gyroscope are compared to evaluate the mode quality. The results show that the kinetic energy in y-axis direction of the drive mode and the kinetic energy in z-axis direction of the sense mode increase with the thickness of the PZT block, and consequently the scale factor of the gyroscope increases. For a constant thickness of the PZT block the scale factor will decrease as the length increases. Through design optimization we present a 20 times improvement in the scale factor of the mode-matched gyroscope. Given the thickness of PZT block, the length and the width will be determined by the mode-matching equations mentioned. Generally, the analysis suggests that the resolution of the gyroscope improves by increasing the thickness PZT block.Comment: 10 pages, 5 figure

Real Root Isolation of Polynomial Equations Based on Hybrid Computation

A new algorithm for real root isolation of polynomial equations based on hybrid computation is presented in this paper. Firstly, the approximate (complex) zeros of the given polynomial equations are obtained via homotopy continuation method. Then, for each approximate zero, an initial box relying on the Kantorovich theorem is constructed, which contains the corresponding accurate zero. Finally, the Krawczyk interval iteration with interval arithmetic is applied to the initial boxes so as to check whether or not the corresponding approximate zeros are real and to obtain the real root isolation boxes. Meanwhile, an empirical construction of initial box is provided for higher performance. Our experiments on many benchmarks show that the new hybrid method is more efficient, compared with the traditional symbolic approaches.Comment: 23 pages. Accepted by ASCM2012. Some typos have been correcte

Optimal Solution of Linear Ordinary Differential Equations by Conjugate Gradient Method

Solving initial value problems and boundary value problems of Linear Ordinary Differential Equations (ODEs) plays an important role in many applications. There are various numerical methods and solvers to obtain approximate solutions represented by points. However, few work about optimal solution to minimize the residual can be found in the literatures. In this paper, we first use Hermit cubic spline interpolation at mesh points to represent the solution, then we define the residual error as the square of the L2 norm of the residual obtained by substituting the interpolation solution back to ODEs. Thus, solving ODEs is reduced to an optimization problem in curtain solution space which can be solved by conjugate gradient method with taking advantages of sparsity of the corresponding matrix. The examples of IVP and BVP in the paper show that this method can find a solution with smaller global error without additional mesh points.Comment: 9 pages,6 figure

Facial Reduction and SDP Methods for Systems of Polynomial Equations

The real radical ideal of a system of polynomials with finitely many complex roots is generated by a system of real polynomials having only real roots and free of multiplicities. It is a central object in computational real algebraic geometry and important as a preconditioner for numerical solvers. Lasserre and co-workers have shown that the real radical ideal of real polynomial systems with finitely many real solutions can be determined by a combination of semi-definite programming (SDP) and geometric involution techniques. A conjectured extension of such methods to positive dimensional polynomial systems has been given recently by Ma, Wang and Zhi. We show that regularity in the form of the Slater constraint qualification (strict feasibility) fails for the resulting SDP feasibility problems. Facial reduction is then a popular technique whereby SDP problems that fail strict feasibility can be regularized by projecting onto a face of the convex cone of semi-definite problems. In this paper we introduce a framework for combining facial reduction with such SDP methods for analyzing $0$ and positive dimensional real ideals of real polynomial systems. The SDP methods are implemented in MATLAB and our geometric involutive form is implemented in Maple. We use two approaches to find a feasible moment matrix. We use an interior point method within the CVX package for MATLAB and also the Douglas-Rachford (DR) projection-reflection method. Illustrative examples show the advantages of the DR approach for some problems over standard interior point methods. We also see the advantage of facial reduction both in regularizing the problem and also in reducing the dimension of the moment matrices. Problems requiring more than one facial reduction are also presented

Structural index reduction algorithms for differential algebraic equations via fixed-point iteration

Motivated by Pryce's structural index reduction method for differential algebraic equations (DAEs), we show the complexity of the fixed-point iteration algorithm and propose a fixed-point iteration method with parameters. It leads to a block fixed-point iteration method which can be applied to large-scale DAEs with block upper triangular structure. Moreover, its complexity analysis is also given in this paper.Comment: 19 page

Recursive Geman-McClure method for implementing second-order Volterra filter

The second-order Volterra (SOV) filter is a powerful tool for modeling the nonlinear system. The Geman-McClure estimator, whose loss function is non-convex and has been proven to be a robust and efficient optimization criterion for learning system. In this paper, we present a SOV filter, named SOV recursive Geman-McClure, which is an adaptive recursive Volterra algorithm based on the Geman-McClure estimator. The mean stability and mean-square stability (steady-state excess mean square error (EMSE)) of the proposed algorithm is analyzed in detail. Simulation results support the analytical findings and show the improved performance of the proposed new SOV filter as compared with existing algorithms in both Gaussian and impulsive noise environments