We propose algorithms for Tucker approximation of 3-tensor, that use it only through tensor-by-vector-by-vector multiplication subroutine. In matrix case, Krylov methods are methods of choice to approximate dominant column and row subspace of sparse or structured matrix given through matrix-by-vector subroutine. However, direct generalization to tensor case, proposed recently by Eldén and Savas...

We give a randomized (Las-Vegas) parallel algorithm for computing strongly connected components of a graph with n vertices andm edges. The runtime is dominated byO(log n) multi-source parallel reachability queries; i.e. O(log n) calls to a subroutine that computes the union of the descendants of a given set of vertices in a given digraph. Our algorithm also topologically sorts the strongly conn...

:' We consider the problem of covering a weighted graph G = (V, E) by a set of " vertex-disjoint paths, such that the total weight of these paths is maximized. This problem is clearly NP-complete, since it contain's the Hamiltonian path problem as a special case. Three approximation algorithms for this problem are presented, exhibiting a complexity-performance trade-off. First, we develop an al...

Skew-Hamiltonian/Hamiltonian matrix pencils λS −H appear in many applications, including linear quadratic optimal control problems, H∞-optimization, certain multi-body systems and many other areas in applied mathematics, physics, and chemistry. In these applications it is necessary to compute certain eigenvalues and/or corresponding deflating subspaces of these matrix pencils. Recently develope...

Large eddy simulations LES based on the Smagorinsky model can be conveniently used in the lattice Boltzmann method LBM because the strain rate tensor, Sij , used to determine the eddy kinematic viscosity can be calculated from the second-order moment of the nonequilibrium distribution function, and the current total nondimensional relaxation time can be determined explicitly. A new method is de...

This paper introduces a probabilistic framework to estimate parameters of an acquisition function given observed human behavior that can be modeled as collection sample paths from Bayesian optimization procedure. The methodology involves defining likelihood on task, where the is parameterized by subroutine governed unknown function. structure enables us make inference subject's while allowing t...

In sheet metal forming processes with complex strain paths, a part is subjected to large plastic deformation. This severe plastic deformation leads to high plastic strain localization zones and subsequent accumulation of those strains. Then internal and superficial micro-defects and in other words ductile damage is created. This damage causes quality problems such as fracture. Therefore, design...

Local causal structure learning aims to discover and distinguish direct causes (parents) effects (children) of a variable interest from data. While emerging successes have been made, existing methods need search large space target T. To tackle this issue, we propose novel Efficient Causal Structure algorithm, named ELCS. Specifically, first the concept N-structures, then design an efficient Mar...

Recently, short fiber reinforced composites are widely utilized in many structural parts in automobile, infrastructure and electric equipment applications due to their high specific strength, high specific stiffness, long fatigue life and high impact resistance as well as cost effective capability of production. If these parts are exposed to low-velocity impact, significant damages may develop ...

An adaptive inteTpTeter for a programming language adapts to particular applications by learning from execution experience. This paper describes PROLEARN, a prototype adaptive interpreter for a subset of Prolog. It uses two methods to speed up a given program: explanation-based generalization and partial evaluation. The generalization of computed results differentiates PROLEARN from programs th...