Elliptic fibers over non-perfect residue fields

Kodaira and Néron classified and described the geometry of the special fibers of the Néron model of an elliptic curve defined over a discrete valuation ring with a perfect residue field. Tate described an algorithm to determine the special fiber type by manipulating the Weierstrass equation. In the case of non-perfect residue fields, we discover new fiber types which are not on the Kodaira-Néron list. We describe these new types and extend Tate's algorithm to deal with all discrete valuation rings. Specifically, we show how to translate a Weierstrass equation into a form where the reduction type may be easily determined. Having determined the special fiber type, we construct the regular model of the curve with explicit blow-up calculations. We also provide tables that serve as a simple reference for the algorithm and which succinctly summarize the results.     

Implicit Cholesky algorithms for singular values and vectors of triangular matrices

The implicit Cholesky algorithm has been developed by several authors during the last 10 years but under different names. We identify the algorithm with a special version of Rutishauser's LR algorithm. Intermediate quantities in the transformation furnish several attractive approximations to the smallest singular value. The paper extols the advantages of using shifts with the algorithm. The nonorthogonal transformations improve accuracy.     

A Special Algorithm for an Additive Model in Data Envelopment Analysis

A special algorithm is presented for the additive model in data envelopment analysis (DEA). The special algorithm first classifies a data set into several subsets. Then the subset is solved by a different algorithmic framework. In simulation studies, the algorithm outperformed available DEA codes. The proposed algorithm can efficiently deal with a large data set.     

On the numerical solution of the quadratic eigenvalue complementarity problem

The Quadratic Eigenvalue Complementarity Problem (QEiCP) is an extension of the Eigenvalue Complementarity Problem (EiCP) that has been introduced recently. Similar to the EiCP, the QEiCP always has a solution under reasonable hypotheses on the matrices included in its definition. This has been established in a previous paper by reducing a QEiCP of dimension n to a special 2n-order EiCP. In this paper we propose an enumerative algorithm for solving the QEiCP by exploiting this equivalence with an EiCP. The algorithm seeks a global minimum of a special Nonlinear Programming Problem (NLP) with a known global optimal value. The algorithm is shown to perform very well in practice but in some cases terminates with only an approximate optimal solution to NLP. Hence, we propose a hybrid method that combines the enumerative method with a fast and local semi-smooth method to overcome the latter drawback. This algorithm is also shown to be useful for computing a positive eigenvalue for an EiCP under similar assumptions. Computational experience is reported to demonstrate the efficacy and efficiency of the hybrid enumerative method for solving the QEiCP.     

Smoothing Newton algorithm for the second-order cone programming with a nonmonotone line search

The smoothing-type algorithms, which are in general designed based on some monotone line search, have been successfully applied to solve the second-order cone programming (denoted by SOCP). In this paper, we propose a nonmonotone smoothing Newton algorithm for solving the SOCP. Under suitable assumptions, we show that the proposed algorithm is globally and locally quadratically convergent. To compare with the existing smoothing-type algorithms for the SOCP, our algorithm has the following special properties: (i) it is based on a new smoothing function of the vector-valued natural residual function; (ii) it uses a nonmonotone line search scheme which contains the usual monotone line search as a special case. Preliminary numerical results demonstrate that the smoothing-type algorithm using the nonmonotone line search is promising for solving the SOCP.     

A Greedy Algorithm for Capacitated Lot-Sizing Problems

We show that the convex hull of the set of feasible solutions of single-item capacitated lot-sizing problem (CLSP) is a base polyhedron of a polymatroid. We present a greedy algorithm to solve CLSP with linear objective function. The proposed algorithm is an effective implementation of the classical Edmonds' algorithm for maximizing linear function over a polymatroid. We consider some special cases of CLSP with nonlinear objective function that can be solved by the proposed greedy algorithm in O ( n ) time.     

A new penalty function algorithm for convex quadratic programming

In this paper, we develop an exterior point algorithm for convex quadratic programming using a penalty function approach. Each iteration in the algorithm consists of a single Newton step followed by a reduction in the value of the penalty parameter. The points generated by the algorithm follow an exterior path that we define. Convergence of the algorithm is established. The proposed algorithm was motivated by the work of Al-Sultan and Murty on nearest point problems, a special quadratic program. A preliminary implementation of the algorithm produced encouraging results. In particular, the algorithm requires a small and almost constant number of iterations to solve the small to medium size problems tested.     

Some n by dn linear complementarity problems

The purpose of this paper is to study some recent applications of the n by dn LCP solvable by a parametric principal pivoting algorithm (PPP algorithm). Often, the LCPs arising from these applications give rise to large systems of linear equations which can be solved fairly efficiently by exploiting their special structures. First, it is shown that by analyzing the n by dn LCP we could study the problem of solving a system of equations and the (nonlinear) complementarity problem when the function involved is separable. Next, we examine conditions under which the PPP algorithm is applicable to a general LCP, and then present examples of LCPs arising from various applications satisfying the conditions; included among them is the n by dn LCP with a certain $\text{P}$-property. Finally we study a special class of n by dn LCPs which do not possess the $\text{P}$-property but to which the PPP algorithm is still applicable; a major application of this class of problems is a certain economic spatial equilibrium model with piecewise linear prices.     

On some structured inverse eigenvalue problems

This work deals with various finite algorithms that solve two special Structured Inverse Eigenvalue Problems (SIEP). The first problem we consider is the Jacobi Inverse Eigenvalue Problem (JIEP): given some constraints on two sets of reals, find a Jacobi matrix J (real, symmetric, tridiagonal, with positive off-diagonal entries) that admits as spectrum and principal subspectrum the two given sets. Two classes of finite algorithms are considered. The polynomial algorithm which is based on a special Euclid–Sturm algorithm (Householder's terminology) and has been rediscovered several times. The matrix algorithm which is a symmetric Lanczos algorithm with a special initial vector. Some characterization of the matrix ensures the equivalence of the two algorithms in exact arithmetic. The results of the symmetric situation are extended to the nonsymmetric case. This is the second SIEP to be considered: the Tridiagonal Inverse Eigenvalue Problem (TIEP). Possible breakdowns may occur in the polynomial algorithm as it may happen with the nonsymmetric Lanczos algorithm. The connection between the two algorithms exhibits a similarity transformation from the classical Frobenius companion matrix to the tridiagonal matrix. This result is used to illustrate the fact that, when computing the eigenvalues of a matrix, the nonsymmetric Lanczos algorithm may lead to a slow convergence, even for a symmetric matrix, since an outer eigenvalue of the tridiagonal matrix of order n − 1 can be arbitrarily far from the spectrum of the original matrix. This revised version was published online in August 2006 with corrections to the Cover Date.     

Parallel solver for trajectory optimization search directions

A key algorithmic element of a real-time trajectory optimization hardware/software implementation is presented, the search step solver. This is one piece of an algorithm whose overall goal is to make nonlinear trajectory optimization fast enough to provide real-time commands during guidance of a vehicle such as an aeromaneuvering orbiter or the National Aerospace Plane. Many methods of nonlinear programming require the solution of a quadratic program (QP) at each iteration to determine the search step. In the trajectory optimization case, the QP has a special dynamic programming structure, an LQR-like structure. The algorithm exploits this special structure with a divide-and-conquer type of parallel implementation. A hypercube message-passing parallel machine, the INTEL iPSC/2, has been used. The algorithm solves a (p·N)-stage problem onN processors inO(p + log₂ N) operations. The algorithm yields a factor of 8 speed-up over the fastest known serial algorithm when solving a 1024-stage test problem on 32 processors.This research was supported in part by the National Aeronautics and Space Administration under Grant No. NAG-1-1009.     

Downwind Gauß-Seidel Smoothing for Convection Dominated Problems

In the case of convection dominated problems, multigrid methods require an appropriate smoothing to ensure robustness. As a first approach we discuss a Gauss–Seidel smoothing with a correct numbering of the unknowns and if necessary a special block partitioning. Numerical experiments show that, in the case of general convection directions, the multigrid algorithms obtained in this way have the same properties as in the model situation. If the graph arising from the convection part is acyclic, we describe a numbering algorithm which is valid for all spatial dimensions. Cycles give rise to special blocks for a blockwise Gauss–Seidel smoothing. We describe an algorithm for the two-dimensional case. The proposed algorithm requires a computational work of optimal order (linear in the size of the problem). © 1997 by John Wiley & Sons, Ltd.     

Generalized GIPSCAL re-revisited: a fast convergent algorithm with acceleration by the minimal polynomial extrapolation

Generalized GIPSCAL, like DEDICOM, is a model for the analysis of square asymmetric tables. It is a special case of DEDICOM, but unlike DEDICOM, it ensures the nonnegative definiteness (nnd) of the model matrix, thereby allowing a spatial representation of the asymmetric relationships among ??objects??. A fast convergent algorithm was developed for GIPSCAL with acceleration by the minimal polynomial extrapolation. The proposed algorithm was compared with Trendafilov??s algorithm in computational speed. The basic algorithm has been adapted to various extensions of GIPSCAL, including off-diagonal DEDICOM/GIPSCAL, and three-way GIPSCAL.     

The continuous grey pattern problem

A new location problem is formulated and solved. It is the continuous version of the grey pattern problem which is a special case of the Quadratic Assignment Problem. The problem is a minimization of a convex function subject to non-convex constraints and has infinitely many optimal solutions. We propose several mathematical programming formulations that are suitable for a multi-start heuristic algorithm. In addition to solving these formulations by the Solver in Excel and Mathematica, a special Nelder–Mead algorithm is proposed. This special algorithm provided the best results. One suggested modification may improve the performance of the Nelder–Mead algorithm for other optimization problems as well.     

A method to accelerate the rate of convergence of a class of optimization algorithms

Supposez ∈ E ⁿ is a solution to the optimization problem minimizeF(x) s.t.x ∈ E ⁿ and an algorithm is available which iteratively constructs a sequence of search directions {s _j } and points {x _j } with the property thatx _j →z. A method is presented to accelerate the rate of convergence of {x _j } toz provided that n consecutive search directions are linearly independent. The accelerating method uses n iterations of the underlying optimization algorithm. This is followed by a special step and then another n iterations of the underlying algorithm followed by a second special step. This pattern is then repeated. It is shown that a superlinear rate of convergence applies to the points determined by the special step. The special step which uses only first derivative information consists of the computation of a search direction and a step size. After a certain number of iterations a step size of one will always be used. The acceleration method is applied to the projection method of conjugate directions and the resulting algorithm is shown to have an (n + 1)-step cubic rate of convergence. The acceleration method is based on the work of Best and Ritter [2].     

Some sufficient conditions for the non-negativity preservation property in the discrete heat conduction model

In the course of the numerical approximation of mathematical models there is often a need to solve a system of linear equations with a tridiagonal or a block-tridiagonal matrices. Usually it is efficient to solve these systems using a special algorithm (tridiagonal matrix algorithm or TDMA) which takes advantage of the structure. The main result of this work is to formulate a sufficient condition for the numerical method to preserve the non-negativity for the special algorithm for structured meshes. We show that a different condition can be obtained for such cases where there is no way to fulfill this condition. Moreover, as an example, the numerical solution of the two-dimensional heat conduction equation on a rectangular domain is investigated by applying Dirichlet boundary condition and Neumann boundary condition on different parts of the boundary of the domain. For space discretization, we apply the linear finite element method, and for time discretization, the well-known Θ-method. The theoretical results of the paper are verified by several numerical experiments.     

SOR- and Jacobi-type iterative methods for solving <Emphasis Type="Italic">ℓ</Emphasis><Subscript>1</Subscript> − <Emphasis Type="Italic">ℓ</Emphasis><Subscript>2</Subscript> problems by way of Fenchel duality

We present an SOR-type algorithm and a Jacobi-type algorithm that can effectively be applied to the ℓ ₁ − ℓ ₂ problem by exploiting its special structure. The algorithms are globally convergent and can be implemented in a particularly simple manner. Relations with coordinate minimization methods are discussed.     

An algorithm for hierarchical optimization of large-scale problems with nested structure

This paper describes a general concept and a particular optimization algorithm for solving a class of large-scale nonlinear programming problems with a nested block-angular structured system of linear constraints with coupling variables. A primal optimization algorithm is developed, which is based on the recursive application of the partitioning concept to the nested structure in combination with a feasible directions method. The special column by column application of this partitioning concept finally leads to a very clear and efficient algorithm for nested problems, which is called ‘successive partitioning method’. It is shown that the reduced-gradient method can be represented as a special application of the concept.     

Multiplication of a Schubert polynomial by a Schur polynomial

Schur polynomials are a special case of Schubert polynomials. In this paper, we give an algorithm to compute the product of a Schubert polynomial with a Schur polynomial on the basis of Schubert polynomials. This is a special case of the general problem of the multiplication of two Schubert polynomials, where the corresponding algorithm is still missing. The main tools for the given algorithm is a factorization property of a special class of Schubert polynomials and the transition formula for Schubert polynomials.     

An efficient algorithm for sparse null space basis problem using ABS methods

We propose a way to use the Markowitz pivot selection criterion for choosing the parameters of the extended ABS class of algorithms to present an effective algorithm for generating sparse null space bases. We explain in detail an efficient implementation of the algorithm, making use of the special MATLAB 7.0 functions for sparse matrix operations and the inherent efficiency of the ANSI C programming language. We then compare our proposed algorithm with an implementation of an efficient algorithm proposed by Coleman and Pothen with respect to the computing time and the accuracy and the sparsity of the generated null space bases. Our extensive numerical results, using coefficient matrices of linear programming problems from the NETLIB set of test problems show the competitiveness of our implemented algorithm.     

具有特殊工件的平行机在线排序问题

本文研究一类具有特殊工件的平行机在线排序问题,目标是最小化最大完工时间.此模型有两种工件:正常工件和特殊工件.正常工件能够在m台平行机的任何一台机器上加工,而特殊工件仅能够在它唯一被指定的机器上加工.文中所有特殊工件的指定机器为M1.我们提供了竞争比为(2m2-2m 1)/(m2-m 1)的在线近似算法.当m=2时,算法是最好可能的.当m=3时,算法的竞争比为13/7≈1.857,并且提供了竞争比的下界(1 (平方根33))14≈1.686.