Now showing items 21-27 of 27
Domain Decomposition for Elliptic Partial Differential Equations with Neumann Boundary Conditions
Discretization of a self-adjoint elliptic partial differential equation by finite differences or finite elements yields a large, sparse, symmetric system of equations, Ax=b. We use the preconditioned conjugate gradient method with domain decomposition to develop an effective, vectorizable preconditioner which is suitable for solving large two-dimensional problems on vector and parallel machines....
Notes on Combinatorial Optimization
A Computational Note on Markov Decision Processes Without Discounting
The Markov decision process is treated in a variety of forms or cases: finite or infinite horizon, with or without discounting. The finite horizon cases and the case of infinite horizon with discounting have received ...
A Feature Preserving Smoother with Application to the Coal-Mining Disaster Data of Britain
The problem of estimating a discontinuous mean function was studied. A feature preserving smoothing procedure was proposed. The procedure can preserve the discontinuities of the function and detect outliers in the observations. ...
A Variable-Metric Variant of the Karmarkar Algorithm for Linear Programming
The most time-consuming part of the Karmarkar algorithm for linear programming is computation of the step direction, which requires the projection of a vector onto the nullspace of a matrix that changes at each iteration. ...
Karmarkar as a Classical Method
In this work we demonstrate that the Karmarkar algorithm for linear programs results from the classical approach of first transforming nonnegativity constraints into equality constraints by adding squared-slack variables ...
Projected Newton for the Symmetric Eigenvalue Problem has Order 1+sqrt(2)
In their study of the classical inverse iteration algorithm, Peters and Wilkinson considered the closely related algorithm that consists of applying Newton's method, followed by a 2-norm normalization, to the nonlinear ...