The nni-distance is a well-known distance measure for phylogenetic trees. We construct an efficient parallel approximation algorithm for the nni-distance in the CRCW-PRAM model running in O(log n) time on O(n) processors. Given two phylogenetic trees T1 and T2 on the same set of taxa and with the same multi-set of edge-weights, the algorithm constructs a sequence of nni-operations of weight at ...

Introduction: This study presents a computerized analyzing method for detection of instantaneous changes of far and near walls of the common carotid artery in sequential ultrasound images by applying the maximum gradient algorithm. Maximum gradient was modified and some characteristics were added from the dynamic programming algorithm for our applications. Methods: The algorithm was evaluat...

This is an experimental computational account of projection algorithms for the linear best approximation problem. We focus on the sequential and simultaneous versions of Dykstra’s algorithm and the Halpern-Lions-Wittmann-Bauschke algorithm for the best approximation problem from a point to the intersection of closed convex sets in the Euclidean space. These algorithms employ different iterative...

The matching problem asks for a large set of disjoint edges in a graph. It is a problem that has received considerable attention in both the sequential and self-stabilizing literature. Previous work has resulted in self-stabilizing algorithms for computing a maximal ( 2 -approximation) matching in a general graph, as well as computing a 3 -approximation on more specific graph types. In the foll...

In this paper we present an interior point algorithm for solving both convex and nonconvex quadratic programs. The method, which is an extension of our interior point work on linear programming problems, efficiently solves a wide class of large scale problems and forms the basis for a sequential quadratic programming (SQP) solver for general large scale nonlinear programs. The key to the algori...

Abstract Many practical problems, such as computer science, communications network, product design, system control, statistics and finance, etc.,can be formulated a probabilistic constrained optimization problem (PCOP) which is challenging to solve since it usually nonconvex nonsmooth. Effective methods for the mostly focus on approximation techniques, convex approximation, D.C. (difference of ...

A technique for maintaining the positive definiteness of the matrices in the quasi-Newton version of the SQP algorithm is proposed. In our algorithm, matrices approximating the Hessian of the augmented Lagrangian are updated. The positive definiteness of these matrices in the space tangent to the constraint manifold is ensured by a so-called piecewise line-search technique, while their positive...

The reduced Hessian SQP algorithm presented in 2] is developed in this paper into a practical method for large-scale optimization. The novelty of the algorithm lies in the incorporation of a correction vector that approximates the cross term Z T WY p Y. This improves the stability and robustness of the algorithm without increasing its computational cost. The paper studies how to implement the a...

Factorial Hidden Markov Models (FHMMs) are powerful models for sequential data but they do not scale well with long sequences. We propose a scalable inference and learning algorithm for FHMMs that draws on ideas from the stochastic variational inference, neural network and copula literatures. Unlike existing approaches, the proposed algorithm requires no message passing procedure among latent v...

|We present a neural network algorithm for minimizing edge crossings in drawings of nonplanar graphs. This is an important subproblem encountered in graph layout. The algorithm nds either the minimum number of crossings or an approximation thereof and also provides a linear embedding realizing the number of crossings found. The parallel time complexity of the algorithm is O(1) for a neural netw...