Single-factor lifting and factorization of polynomials over local fields

Let f(x)$f\left(x\right)$ be a separable polynomial over a local field. The Montes algorithm computes certain approximations to the different irreducible factors of f(x)$f\left(x\right)$, with strong arithmetic properties. In this paper, we develop an algorithm to improve any one of these approximations, till a prescribed precision is attained. The most natural application of this “single-factor lifting” routine is to combine it with the Montes algorithm to provide a fast polynomial factorization algorithm. Moreover, the single-factor lifting algorithm may be applied as well to accelerate the computational resolution of several global arithmetic problems in which the improvement of an approximation to a single local irreducible factor of a polynomial is required.     

Approximate factorization of multivariate polynomials using singular value decomposition

We describe the design, implementation and experimental evaluation of new algorithms for computing the approximate factorization of multivariate polynomials with complex coefficients that contain numerical noise. Our algorithms are based on a generalization of the differential forms introduced by W. Ruppert and S. Gao to many variables, and use singular value decomposition or structured total least squares approximation and Gauss–Newton optimization to numerically compute the approximate multivariate factors. We demonstrate on a large set of benchmark polynomials that our algorithms efficiently yield approximate factorizations within the coefficient noise even when the relative error in the input is substantial (10⁻³).     

Factoring polynomials over global fields II

In this paper we describe software for an efficient factorization of polynomials over global fields F. The algorithm for function fields was recently incorporated into our system KANT. The method is based on a generic algorithm developed by the second author in an earlier paper in this journal. Besides algorithmic aspects not contained in that paper we give details about the current implementation and about some complexity issues as well as a few illustrative examples. Also, a generalization of the application of LLL reduction for factoring polynomials over arbitrary global fields is developed.     

Generalized Lyapunov equation and factorization of matrix polynomials

This paper presents an algorithm for the construction of a solution of the generalized Lyapunov equation. It is proved that the polynomial matrix factorization relative to the imaginary axis may be reduced to the successive solution of Lyapunov equations, i.e. the factorization is reduced to the solution of a sequence of generalized Lyapunov equations, not to the solution of generalized Riccati equation.     

On factorization forests of finite height

Simon (Theoret. Comput. Sci. 72 (1990) 65–94) has proved that every morphism from a free semigroup to a finite semigroup S admits a Ramseyan factorization forest of height at most 9|S|. In this paper, we prove the same result of Simon with an improved bound of 7|S|. We provide a simple algorithm for constructing a factorization forest. In addition, we show that the algorithm cannot be improved significantly. We give examples of semigroup morphism such that any Ramseyan factorization forest for the morphism would require a height not less than |S|.     

New algorithms for generating Conway polynomials over finite fields

Arithmetic in a finite field of prime characteristic p normally employs an irreducible polynomial in $\text{Z}{}_{\mathrm{p}}\text{[X]}$. A particular class of irreducible polynomials, generally known as Conway polynomials, provides a means for representing several finite fields of characteristic p in a compatible manner. Conway polynomials are used in computational algebra systems such as GAP and Magma to represent finite fields. The generation of the Conway polynomial for a particular finite field has previously been done by an often expensive brute force search. We present two new algorithms for generating Conway polynomials that avoid the brute force search. We have implemented one of these algorithms in Magma and present numerous new Conway polynomials generated by our implementation.     

Consensus networks over finite fields

This work studies consensus strategies for networks of agents with limited memory, computation, and communication capabilities. We assume that agents can process only values from a finite alphabet, and we adopt the framework of finite fields, where the alphabet consists of the integers {0,…,p−1}$\{0,\dots ,p-1\}$, for some prime number p$p$, and operations are performed modulo p$p$. Thus, we define a new class of consensus dynamics, which can be exploited in certain applications such as pose estimation in capacity and memory constrained sensor networks. For consensus networks over finite fields, we provide necessary and sufficient conditions on the network topology and weights to ensure convergence. We show that consensus networks over finite fields converge in finite time, a feature that can be hardly achieved over the field of real numbers. For the design of finite-field consensus networks, we propose a general design method, with high computational complexity, and a network composition rule to generate large consensus networks from smaller components. Finally, we discuss the application of finite-field consensus networks to distributed averaging and pose estimation in sensor networks.     

Linear structures of symmetric functions over finite fields

It is shown that nonlinear symmetric functions over finite fields GF(p) have no linear structures other than equal component vectors.     

Towards toric absolute factorization

This article presents an algorithmic approach to study and compute the absolute factorization of a bivariate polynomial, taking into account the geometry of its monomials. It is based on algebraic criterions inherited from algebraic interpolation and toric geometry.     

Efficient bit-parallel multipliers over finite fields GF(2)

Hardware implementation of multiplication in finite field GF(2^m) based on sparse polynomials is found to be advantageous in terms of space-complexity as well as the time-complexity. In this paper, we present a new permutation method to construct the irreducible like-trinomials of the form (x + 1)^m + (x + 1)ⁿ + 1 for the implementation of efficient bit-parallel multipliers. For implementing the multiplications based on such polynomials, we have defined a like-polynomial basis (LPB) as an alternative to the original polynomial basis of GF(2^m). We have shown further that the modular arithmetic for the binary field based on like-trinomials is equivalent to the arithmetic for the field based on trinomials. In order to design multipliers for composite fields, we have found another permutation polynomial to convert irreducible polynomials into like-trinomials of the forms (x² + x + 1)^m + (x² + x + 1)ⁿ + 1, (x² + x)^m + (x² + x)ⁿ + 1 and (x⁴ + x + 1)^m + (x⁴ + x + 1)ⁿ + 1. The proposed bit-parallel multiplier over GF(2^4m) is found to offer a saving of about 33% multiplications and 42.8% additions over the corresponding existing architectures.     

矩阵的Doolittle递归分解算法及符号程序设计

将矩阵An×n的Doolittle分解推广到Am×n上，并在常规的迭代算法上加以创新，给出了递归的分解算法．在实现算法的过程中，对数据进行了巧妙处理，使中间数据及最终计算结果都具有分数形式，提高了结果的精确度，而且更符合人们阅读的习惯．经过运行测试，算法设计合理，程序运行高效准确．程序是对MathSoft公司的交互式的数学文字软件Mathcad的矩阵分解的数值计算扩充到符号运算．     

Performance of parallel Cholesky factorization algorithms using BLAS

This paper considers four parallel Cholesky factorization algorithms, including SPOTRF from the February 1992 release of LAPACK, each of which call parallel Level 2 or 3 BLAS, or both. A fifth parallel Cholesky algorithm that calls serial Level 3 BLAS is also described. The efficiency of these five algorithms on the CRAY-2, CRAY Y-MP/832, Hitachi Data Systems EX 80, and IBM 3090-600J is evaluated and compared with a vendor-optimized parallel Cholesky factorization algorithm. The fifth parallel Cholesky algorithm that calls serial Level 3 BLAS provided the best performance of all algorithms that called BLAS routines. In fact, this algorithm outperformed the Cray-optimized libsci routine (SPOTRF) by 13–44%;, depending on the problem size and the number of processors used.This work was supported by grants from IMSL, Inc., and Hitachi Data Systems. The first version of this paper was presented as a poster session at Supercomputing '90, New York City, November 1990.     

Characteristic set algorithms for equation solving in finite fields

Efficient characteristic set methods for computing zeros of polynomial equation systems in a finite field are proposed. The concept of proper triangular sets is introduced and an explicit formula for the number of zeros of a proper and monic triangular set is given. An improved zero decomposition algorithm is proposed to reduce the zero set of an equation system to the union of zero sets of monic proper triangular sets. The bitsize complexity of this algorithm is shown to be O(lⁿ)$O\left(l^n\right)$ for Boolean polynomials, where n$n$ is the number of variables and l≥2$l\ge 2$ is the number of equations. We also give a multiplication free characteristic set method for Boolean polynomials, where the sizes of the polynomials occurred during the computation do not exceed the sizes of the input polynomials and the bitsize complexity of algorithm is O(n^d)$O\left(n^d\right)$ for input polynomials with n$n$ variables and degree d$d$. The algorithms are implemented in the case of Boolean polynomials and extensive experiments show that they are quite efficient for solving certain classes of Boolean equations raising from stream ciphers.     

Modular Las Vegas algorithms for polynomial absolute factorization

Let f(X,Y)∈Z[X,Y]$f(X,Y)\in \mathbb{Z}[X,Y]$ be an irreducible polynomial over Q$\mathbb{Q}$. We give a Las Vegas absolute irreducibility test based on a property of the Newton polytope of f$f$, or more precisely, of f$f$ modulo some prime integer p$p$. The same idea of choosing a p$p$ satisfying some prescribed properties together with LLL$LLL$ is used to provide a new strategy for absolute factorization of f(X,Y)$f(X,Y)$. We present our approach in the bivariate case but the techniques extend to the multivariate case. Maple computations show that it is efficient and promising as we are able to construct the algebraic extension containing one absolute factor of a polynomial of degree up to 400.     

On multivariate polynomial matrix factorization problems

This paper studies the multivariate polynomial matrix factorization problems which have applications to multidimensional systems theory and signal processing. We first extract an algorithm from Pommaret's proof of the Lin-Bose conjecture. Then we simplify our algorithm, and prove a theorem which gives a sufficient and necessary condition for a multivariate polynomial matrix to have an minor left prime (MLP) factorization. Examples are given to illustrate the effectiveness of this algorithm. Our results hold for any coefficient field and thus have a wide range of applications. This work was supported by grants from the Research Grants Council of Hong Kong (Project CUHK4185/01E) and 973 projects(G1999035802 and 2004CB318004)     

Decomposition of algebras over finite fields and number fields

We consider the boolean complexity of the decomposition of semi-simple algebras over finite fields and number fields.We present new polynomial time algorithms for the decomposition of semi-simple algebras over these fields. Our algorithms are somewhat simpler than previous algorithms, and provide parallel reductions from semi-simple decomposition to the factorization of polynomials. As a consequence we obtain efficient parallel algorithms for the decomposition of semi-simple algebras over small finite fields. We also present efficient sequential and parallel algorithms for the decomposition of a simple algebra from a basis and a primitive idempotent. These will be applied in a subsequent paper to obtain Las Vegas polynomial time algorithms for the decomposition of matrix algebras over and .