Through the use of the well-known Morrison/Wheat/Erkut ice hockey model, we demonstrate that even optimal solutions may be open to question. Based on the assumptions which are made to estimate data values, optimal solutions may vary among decision makers. We also note that under any reasonable set of assumptions, hockey coaches wait too long before pulling their goalies. tion made by Erkut is a...

We investigate high density state of SU(2) QCD by using Lattice QCD simulation with Wilson fermions. The ratio of fermion determinants is evaluated at each step of the Metropolis link update by Woodbury formula. At β = 0.7, and κ = 0.150, we calculate the baryon number density, the Polyakov lines, and the energy density of gluon sector with chemical potential μ=0 to 0.8 on the 43 × 12 lattice. ...

In transient simulations of particulate Stokes flow, to accurately capture the interaction between constituent particles and confining wall, discretization wall often needs be locally refined in region approached by particles. Consequently, standard fast direct solvers lose their efficiency since linear system changes at each time step. This manuscript presents a new computational approach that...

We present a new Hessian estimator based on the simultaneous perturbation procedure, that requires three system simulations regardless of the parameter dimension. We then present two Newton-based simulation optimization algorithms that incorporate this Hessian estimator. The two algorithms differ primarily in the manner in which the Hessian estimate is used. Both our algorithms do not compute t...

In this paper we improve the BIF algorithm which computes simultaneously the LU factors (direct factors) of a given matrix, and their inverses (inverse factors). This algorithm was introduced in [R. Bru, J. Maŕın, J. Mas and M. Tůma, SIAM J. Sci. Comput., 30 (2008), pp. 2302– 2318]. The improvements are based on a deeper understanding of the Inverse Sherman-Morrison (ISM) decomposition and they...

The Gustafson-Kessel fuzzy clustering algorithm is capable of detecting hyperellipsoidal clusters of different sizes and orientations by adjusting the covariance matrix of data, thus overcoming the drawbacks of conventional fuzzy c-means algorithm. In this paper, an adaptive version of the Gustafson-Kessel algorithm is proposed. The way to adjust the covariance matrix iteratively is introduced ...

A fast algorithm for solving systems of linear equations with banded Toeplitz matrices is studied. An important step in the algorithm is a novel method for the spectral factorization of the generating function associated with the Toeplitz matrix. The spectral factorization is extracted from the right deflating subspaces corresponding to the eigenvalues inside and outside the open unit disk of a...

This paper presents two methods based on domain decomposition concepts for determining the diagonal of the inverse of a sparse matrix. The first uses a divide-and-conquer principle and the ShermanMorrison-Woodbury formula, and assumes that the matrix can be decomposed into a 2 × 2 block-diagonal matrix and a low-rank matrix. The second method is a standard domain decomposition approach in which...

We present an efficient low-rank updating algorithm for updating the trial wave functions used in quantum Monte Carlo (QMC) simulations. The algorithm is based on low-rank updating of the Slater determinants. In particular, the computational complexity of the algorithm is O(kN) during the kth step compared to traditional algorithms that require O(N(2)) computations, where N is the system size. ...

In this article, we consider solvers for large-scale trust-region subproblems when the quadratic model is defined by a limited-memory symmetric rank-one (L-SR1) quasi-Newton matrix. We propose a solver that exploits the compact representation of L-SR1 matrices. Our approach makes use of both an orthonormal basis for the eigenspace of the L-SR1 matrix and the ShermanMorrison-Woodbury formula to ...