arXiv subjects

Wenyuan Wu

Publications and source records attributed to Wenyuan Wu.

36 records · Page 2Linked to original sources

arXiv2023

Structural Analysis by Modified Signature Matrix for Integro-differential-algebraic Equations

Integro-differential-algebraic equations (IDAE)s are widely used in applications of engineering and analysis. When there are hidden constraints in an IDAE, structural analysis is necessary. But if derivatives of dependent variables appear in their integrals, the existing definition of the signature matrix for an IDAE cannot be satisfied. Moreover, if an IDAE has a singular Jacobian matrix after structural analysis by the Sigma-method, improved structural analysis methods are proposed to regularize it. However, the optimal value of an IDAE may be negative which can not ensure the termination of the regularization. Furthermore, overestimation of the signature matrix may also lead to failure of its structural analysis. In this paper, firstly, we redefine the signature matrix and introduce a definition of the degree of freedom for IDAEs. Thus, the termination of improved structural analysis methods can be guaranteed. Secondly, the detection method by points is proposed to deal with the problem of overestimation of signature matrix. Thirdly, the embedding method has proved to suitable for structural unamenable IDAEs, including those types that arise from symbolic cancellation and numerical degeneration. Finally, the global numerical method is applied to an example of two-stage drive system which can help to find all solutions for IDAEs by witness points. Hopefully, through the example of pendulum curtain, the approach for IDAEs proposed in this paper can be applied to integro-partial-differential-algebraic equations (IPDAE)s.

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arXiv2023

On the probability of generating a primitive matrix

Given a $k\times n$ integer primitive matrix $\bf{A}$ (i.e., a matrix can be extended to an $n\times n$ unimodular matrix over the integers) with the maximal absolute value of entries $\|\bf{A}\|$ bounded by {an integer} $λ$ from above, we study the probability that the $m\times n$ matrix extended from $\bf{A}$ by appending other $m-k$ row vectors of dimension $n$ with entries chosen randomly and independently from the uniform distribution over $\{0, 1,\ldots, λ-1\}$ is still primitive. We present a complete and rigorous proof of a lower bound on the probability, which is at least a constant for fixed $m$ in the range $[k+1, n-4]$. As an application, we prove that there exists a fast Las Vegas algorithm that completes a $k\times n$ primitive matrix $\bf{A}$ to an $n\times n$ unimodular matrix within expected $\tilde{O}(n^ω\log \|\bf{A}\|)$ bit operations, where $\tilde{O}$ is big-$O$ but without log factors, $ω$ is the exponent on the arithmetic operations of matrix multiplication.

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arXiv2022

Index Reduction for Degenerated Differential-Algebraic Equations by Embedding

To find consistent initial data points for a system of differential-algebraic equations, requires the identification of its missing constraints. An efficient class of structural methods exploiting a dependency graph for this task was initiated by Pantiledes. More complete methods rely on differential-algebraic geometry but suffer from other issues (e.g. high complexity). In this paper we give a new class of efficient structural methods combined with new tools from numerical real algebraic geometry that has much improved completeness properties. Existing structural methods may fail for a system of differential-algebraic equations if its Jacobian matrix after differentiation is still singular due to symbolic cancellation or numerical degeneration. Existing structural methods can only handle degenerated cases caused by symbolic cancellation. However, if a system has parameters, then its parametric Jacobian matrix may be still singular after application of the structural method for certain values of the parameters. This case is called numerical degeneration. For polynomially nonlinear systems of differential-algebraic equations, numerical methods are given to solve both degenerated cases using numerical real algebraic geometry. First, we introduce a witness point method, which produces at least one witness point on every constraint component. This can help to ensure constant rank and detection of degeneration on all components of such systems. Secondly, we present a Constant Rank Embedding Lemma, and based on it propose an Index Reduction by Embedding (IRE) method which can construct an equivalent system with a full rank Jacobian matrix. Thirdly, IRE leads to a global structural differentiation method, to solve degenerated differential-algebraic equations on all components numerically. Application examples from circuits, mechanics, are used to demonstrate our method.

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arXiv2021

The numerical factorization of polynomials

Polynomial factorization in conventional sense is an ill-posed problem due to its discontinuity with respect to coefficient perturbations, making it a challenge for numerical computation using empirical data. As a regularization, this paper formulates the notion of numerical factorization based on the geometry of polynomial spaces and the stratification of factorization manifolds. Furthermore, this paper establishes the existence, uniqueness, Lipschitz continuity, condition number, and convergence of the numerical factorization to the underlying exact factorization, leading to a robust and efficient algorithm with a MATLAB implementation capable of accurate polynomial factorizations using floating point arithmetic even if the coefficients are perturbed.

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arXiv2021

A Companion Curve Tracing Method for Rank-deficient Polynomial Systems

We propose a method for tracing implicit real algebraic curves defined by polynomials with rank-deficient Jacobians. For a given curve $f^{-1}(0)$, it first utilizes a regularization technique to compute at least one witness point per connected component of the curve. We improve this step by establishing a sufficient condition for testing the emptiness of $f^{-1}(0)$. We also analyze the convergence rate and carry out an error analysis for refining the witness points. The witness points are obtained by computing the minimum distance of a random point to a smooth manifold embedding the curve while at the same time penalizing the residual of $f$ at the local minima. To trace the curve starting from these witness points, we prove that if one drags the random point along a trajectory inside a tubular neighborhood of the embedded manifold of the curve, the projection of the trajectory on the manifold is unique and can be computed by numerical continuation. We then show how to choose such a trajectory to approximate the curve by computing eigenvectors of certain matrices. Effectiveness of the method is illustrated by examples.

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arXiv2019

Visualizing Planar and Space Implicit Real Algebraic Curves with Singularities

We present a new method for visualizing implicit real algebraic curves inside a bounding box in the $2$-D or $3$-D ambient space based on numerical continuation and critical point methods. The underlying techniques work also for tracing space curve in higher-dimensional space. Since the topology of a curve near a singular point of it is not numerically stable, we trace only the curve outside neighborhoods of singular points and replace each neighborhood simply by a point, which produces a polygonal approximation that is $ε$-close to the curve. Such an approximation is more stable for defining the numerical connectedness of the complement of the projection of the curve in $\mathbb{R}^2$, which is important for applications such as solving bi-parametric polynomial systems. The algorithm starts by computing three types of key points of the curve, namely the intersection of the curve with small spheres centered at singular points, regular critical points of every connected components of the curve, as well as intersection points of the curve with the given bounding box. It then traces the curve starting with and in the order of the above three types of points. This basic scheme is further enhanced by several optimizations, such as grouping singular points in natural clusters, tracing the curve by a try-and-resume strategy and handling "pseudo singular points". The effectiveness of the algorithm is illustrated by numerous examples. This manuscript extends our preliminary results that appeared in CASC 2018.

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arXiv2018

The PSLQ Algorithm for Empirical Data

The celebrated integer relation finding algorithm PSLQ has been successfully used in many applications. PSLQ was only analyzed theoretically for exact input data, however, when the input data are irrational numbers, they must be approximate ones due to the finite precision of the computer. When the algorithm takes empirical data (inexact data with error bounded) instead of exact real numbers as its input, how do we theoretically ensure the output of the algorithm to be an exact integer relation? In this paper, we investigate the PSLQ algorithm for empirical data as its input. Firstly, we give a termination condition for this case. Secondly, we analyze a perturbation on the hyperplane matrix constructed from the input data and hence disclose a relationship between the accuracy of the input data and the output quality (an upper bound on the absolute value of the inner product of the exact data and the computed integer relation), which naturally leads to an error control strategy for PSLQ. Further, we analyze the complexity bound of the PSLQ algorithm for empirical data. Examples on transcendental numbers and algebraic numbers show the meaningfulness of our error control strategy.

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arXiv2018

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.

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arXiv2018

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.

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arXiv2017

A Special Homotopy Continuation Method For A Class of Polynomial Systems

A special homotopy continuation method, as a combination of the polyhedral homotopy and the linear product homotopy, is proposed for computing all the isolated solutions to a special class of polynomial systems. The root number bound of this method is between the total degree bound and the mixed volume bound and can be easily computed. The new algorithm has been implemented as a program called LPH using C++. Our experiments show its efficiency compared to the polyhedral or other homotopies on such systems. As an application, the algorithm can be used to find witness points on each connected component of a real variety.

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arXiv2015

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.

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arXiv2014

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.

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arXiv2014

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.

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arXiv2014

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.

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arXiv2013

Numerical method for real root isolation of semi-algebraic system and its applications

In this paper, based on the homotopy continuation method and the interval Newton method, an efficient algorithm is introduced to isolate the real roots of semi-algebraic system. Tests on some random examples and a variety of problems including transcendental functions arising in many applications show that the new algorithm reduces the cost substantially compared with the traditional symbolic approaches.

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arXiv2012

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.

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arXiv2012

Structural analysis of high-index DAE for process simulation

This paper deals with the structural analysis problem of dynamic lumped process high-index DAE models. We consider two methods for index reduction of such models by differentiation: Pryce's method and the symbolic differential elimination algorithm rifsimp. Discussion and comparison of these methods are given via a class of fundamental process simulation examples. In particular, the efficiency of the Pryce method is illustrated as a function of the number of tanks in process design.

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arXiv2010

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.

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