A fast algorithm for minimum weight odd circuits and cuts in planar graphs

We give a simple algorithm for finding a minimum weight odd circuit in planar graphs. By geometric duality, the same algorithm can be used to find minimum weight odd cuts. For general sparse graphs, the fastest known algorithms for these two problems take time and time, respectively.     

A very fast algorithm for matrix factorization

We present a very fast algorithm for general matrix factorization of a data matrix for use in the statistical analysis of high-dimensional data via latent factors. Such data are prevalent across many application areas and generate an ever-increasing demand for methods of dimension reduction in order to undertake the statistical analysis of interest. Our algorithm uses a gradient-based approach which can be used with an arbitrary loss function provided the latter is differentiable. The speed and effectiveness of our algorithm for dimension reduction is demonstrated in the context of supervised classification of some real high-dimensional data sets from the bioinformatics literature.     

A new algorithm for generation of permutations

A new algorithm for generating permutations is presented, that generates the next permutation by reversing a certain suffix of its predecessor. The average size of this suffix is less thane 2.8. It is shown how to find the position of a given permutation and how to construct the permutation of a given position, where the position refers to the order in which the permutations are generated, and is also new.On leave from the Department of Computer Science, Technion, Haifa, Israel. This work was supported by NSF grant MCS-8302391.     

A simplified loop-free algorithm for generating permutations

A simple loop-free algorithm for generation of all permutations of a set of elements is presented and its validity is proved. It is a simplification of Ehrlich's loop-free version of Johnson and Trotter's algorithm. Each permutation is generated by exchanging two adjacent elements of the preceding permutation. A very simple data structure obviates the need for looping during the generation of each successive permutation.     

A new arc algorithm for unconstrained optimization

The gradient path of a real valued differentiable function is given by the solution of a system of differential equations. For a quadratic function the above equations are linear, resulting in a closed form solution. A quasi-Newton type algorithm for minimizing ann-dimensional differentiable function is presented. Each stage of the algorithm consists of a search along an arc corresponding to some local quadratic approximation of the function being minimized. The algorithm uses a matrix approximating the Hessian in order to represent the arc. This matrix is updated each stage and is stored in its Cholesky product form. This simplifies the representation of the arc and the updating process. Quadratic termination properties of the algorithm are discussed as well as its global convergence for a general continuously differentiable function. Numerical experiments indicating the efficiency of the algorithm are presented.     

A graph-theoretic model to solve the approximate string matching problem allowing for translocations

In this paper, we study the approximate string matching problem under a string distance whose edit operations are translocations of equal length factors. We extend a graph-theoretic approach proposed by Rahman and Illiopoulos (2008) to model our problem. In the sequel, we devise efficient algorithms based on this model to solve a number of variants of the string matching problem with translocations.     

A variable dimension algorithm for the linear complementarity problem

A variable dimension algorithm is presented for the linear complementarity problems – Mz = q; s,z 0; s _i z _i = 0 fori = 1,2, ,n. The algorithm solves a sequence of subproblems of different dimensions, the sequence being possibly nonmonotonic in the dimension of the subproblem solved. Every subproblem is the linear complementarity problem defined by a leading principal minor of the matrixM. Index-theoretic arguments characterize the points at which nonmonotonic behavior occurs.     

A linear-time algorithm for finding optimal vehicle refueling policies

We explore a fixed-route vehicle refueling problem as a special case of the inventory-capacitated lot-sizing problem, and present a linear-time greedy algorithm for finding optimal refueling policies.     

A fast algorithm for balanced sampling

Summary  The cube method (Deville & Tillé 2004) is a large family of algorithms that allows selecting balanced samples with equal or unequal inclusion probabilities. In this paper, we propose a very fast implementation of the cube method. The execution time does not depend on the square of the population size anymore, but only on the population size. Balanced samples can thus be selected in very large populations of several hundreds of thousands of units.     

A fast algorithm for equitable coloring

A proper vertex coloring of a graph is equitable if the sizes of color classes differ by at most one. The celebrated Hajnal-Szemerédi Theorem states: For every positive integer r, every graph with maximum degree at most r has an equitable coloring with r+1 colors. We show that this coloring can be obtained in O(rn ²) time, where n is the number of vertices.     

A fast algorithm for image registration

An algorithm is presented here to estimate a smooth motion at a high frame rate. It is derived from the non-linear constant brightness assumption. A hierarchical approach reduces the dimension of the space of admissible displacements, hence the number of unknown parameters is small compared to the size of the data. The optimal displacement is estimated by a Gauss–Newton method, and the matrix required at each step is assembled rapidly using a finite-element method. To cite this article: J. Fehrenbach, M. Masmoudi, C. R. Acad. Sci. Paris, Ser. I 346 (2008).     

A fast algorithm for multivariate Hermite interpolation

Multivariate Hermite interpolation is widely applied in many fields, such as finite element construction, inverse engineering, CAD etc.. For arbitrarily given Hermite interpolation conditions, the typical method is to compute the vanishing ideal I （the set of polynomials satisfying all the homogeneous interpolation conditions are zero） and then use a complete residue system modulo I as the interpolation basis. Thus the interpolation problem can be converted into solving a linear equation system. A generic algorithm was presented in [18], which is a generalization of BM algorithm [22] and the complexity is O（τ^3） where r represents the number of the interpolation conditions. In this paper we derive a method to obtain the residue system directly from the relative position of the points and the corresponding derivative conditions （presented by lower sets） and then use fast GEPP to solve the linear system with O（（τ ＋ 3）τ^2） operations, where τ is the displacement-rank of the coefficient matrix. In the best case τ = 1 and in the worst case τ = [τ/n], where n is the number of variables.     

A fast algorithm for scalar Nevanlinna-Pick interpolation

Summary In this paper, we derive a fast algorithm for the scalar Nevanlinna-Pick interpolation. Givenn distinct pointsz _i in the unit disk |z|<1 andn complex numbersw _i satisfying the Pick condition for 1in, the new Nevanlinna-Pick interpolation algorithm requires onlyO(n) arithmetic operations to evaluate the interpolatory rational function at a particular value ofz, in contrast to the classical algorithm which requiresO(n ²) arithmetic operations to compute the so-called Fenyves array (which is inherent in the classical algorithm). The new algorithm bypasses the generation of the Fenyves array to speed up the computation, and also yields a parallel scheme requiring onlyO(logn) arithmetic operations on a concurrent-read, exclusive-write parallel random access machine withn processors. We must remark that the rational functionf(z) computed by the new algorithm is one degree higher than the function computed by the classical algorithm.Supported in part by the US Army Research Office Grant No. DAAL03-91-G-0106     

A fast algorithm for coloring Meyniel graphs

We color the nodes of a graph by first applying successive contractions to non-adjacent nodes until we get a clique; then we color the clique and decontract the graph. We show that this algorithm provides a minimum coloring and a maximum clique for any Meyniel graph by using a simple rule for choosing which nodes are to be contracted. This O(n³) algorithm is much simpler than those already existing for Meyniel graphs. Moreover, the optimality of this algorithm for Meyniel graphs provides an alternate nice proof of the following result of Hoàng: a graph G is Meyniel if and only if, for any induced subgraph of G, each node belongs to a stable set that meets all maximal cliques. Finally we give a new characterization for Meyniel graphs.     

A primal conical linear programming algorithm

This paper completes the treatment of the conical approach to linear programming, introducing a conical primal algorithm of linear programming. After some recalls and improvements of a previous paper dealing with such an approach, the algorithm is defined. A first convergence result is then proved, which, along with a series of lemmata, allows to prove that the algorithm terminates in a finite number of steps     

A practical algorithm for faster matrix multiplication

The purpose of this paper is to present an algorithm for matrix multiplication based on a formula discovered by Pan [7]. For matrices of order up to 10 000, the nearly optimum tuning of the algorithm results in a rather clear non‐recursive one‐ or two‐level structure with the operation count comparable to that of the Strassen algorithm [9]. The algorithm takes less workspace and has better numerical stability as compared to the Strassen algorithm, especially in Winograd's modification [2]. Moreover, its clearer and more flexible structure is potentially more suitable for efficient implementation on modern supercomputers. Copyright © 1999 John Wiley & Sons, Ltd.     

A mean value algorithm for Toeplitz matrix completion

In this paper, we propose a new mean value algorithm for the Toeplitz matrix completion based on the singular value thresholding (SVT) algorithm. The completion matrices generated by the new algorithm keep a feasible Toeplitz structure. Meanwhile, we prove the convergence of the new algorithm under some reasonal conditions. Finally, we show the new algorithm is much more effective than the ALM (augmented Lagrange multiplier) algorithm through numerical experiments and image inpainting.     

A fast algorithm for solving a Toeplitz system

In this paper, we present a fast algorithm for solving Symmetric penta-diagonal systems. We give the feasibility and Stability analysis of the algorithm. Moreover, parallel computations can be implemented in the algorithm. The numerical examples verify the efficiency of the algorithm.     

Learning about multiplication by comparing algorithms: “One times one,but actually they are ten times ten”

Multiplication algorithms in primary school are still frequently introduced with little attention to meaning. We present a case study focusing on a third grade class that engaged in comparing two algorithms and discussing “why they both work”. The objectives of the didactical intervention were to foster students' development of mathematical meanings concerning multiplication algorithms, and their development of an attitude to judge and compare the value and efficiency of different algorithms. Underlying hypotheses were that it is possible to promote the simultaneous unfolding of the semiotic potential of two algorithms, considered as cultural artifacts, with respect to the objectives of the didactical intervention, and to establish a fruitful synergy between the two algorithms. As results, this study sheds light onto the new theoretical construct of “bridging sign”, illuminating students’ meaning-making processes involving more than one artifact; and it provides important insight into the actual unfolding of the hypothesized potential of the algorithms.     

A fast computational algorithm for the Legendre-Fenchel transform

We investigate a fast algorithm, introduced by Brenier, which computes the Legendre-Fenchel transform of a real-valued function. We generalize his work to boxed domains and introduce a parameter in order to build an iterative algorithm. The new approach of separating primal and dual spaces allows a clearer understanding of the algorithm and yields better numerical behavior. We extend known complexity results and give new ones about the convergence of the algorithm.