Numerical Methods for Grid Equations

This item is printed on demand - it takes 3-4 days longer - Neuware -5 The Mathematical Theory of Iterative Methods.- 5.1 Several results from functional analysis.- 5.1.1 Linear spaces.- 5.1.2 Operators in linear normed spaces.- 5.1.3 Operators in a Hilbert space.- 5.1.4 Functions of a bounded operator.- 5.1.5 Operators in a finite-dimensional space.- 5.1.6 The solubility of operator equations.- 5.2 Difference schemes as operator equations.- 5.2.1 Examples of grid-function spaces.- 5.2.2 Several difference identities.- 5.2.3 Bounds for the simplest difference operators.- 5.2.4 Lower bounds for certain difference operators.- 5.2.5 Upper bounds for difference operators.- 5.2.6 Difference schemes as operator equations in abstract spaces.- 5.2.7 Difference schemes for elliptic equations with constant coefficients.- 5.2.8 Equations with variable coefficients and with mixed derivatives.- 5.3 Basic concepts from the theory of iterative methods.- 5.3.1 The steady state method.- 5.3.2 Iterative schemes.- 5.3.3 Convergence and iteration counts.- 5.3.4 Classification of iterative methods.- 6 Two-Level Iterative Methods.- 6.1 Choosing the iterative parameters.- 6.1.1 The initial family of iterative schemes.- 6.1.2 The problem for the error.- 6.1.3 The self-adjoint case.- 6.2 The Chebyshev two-level method.- 6.2.1 Construction of the set of iterative parameters.- 6.2.2 On the optimality of the a priori estimate.- 6.2.3 Sample choices for the operator D.- 6.2.4 On the computational stability of the method.- 6.2.5 Construction of the optimal sequence of iterative parameters.- 6.3 The simple iteration method.- 6.3.1 The choice of the iterative parameter.- 6.3.2 An estimate for the norm of the transformation operator.- 6.4 The non-self-adjoint case. The simple iteration method.- 6.4.1 Statement of the problem.- 6.4.2 Minimizing the norm of the transformation operator.- 6.4.3 Minimizing the norm of the resolving operator.- 6.4.4 The symmetrization method.- 6.5 Sample applications of the iterative methods.- 6.5.1 A Dirichlet difference problem for Poisson¿s equation in a rectangle.- 6.5.2 A Dirichlet difference problem for Poisson¿s equation in an arbitrary region.- 6.5.3 A Dirichlet difference problem for an elliptic equation with variable coefficients.- 6.5.4 A Dirichlet difference problem for an elliptic equation with mixed derivatives.- 7 Three-Level Iterative Methods.- 7.1 An estimate of the convergence rate.- 7.1.1 The basic family of iterative schemes.- 7.1.2 An estimate for the norm of the error.- 7.2 The Chebyshev semi-iterative method.- 7.2.1 Formulas for the iterative parameters.- 7.2.2 Sample choices for the operator D.- 7.2.3 The algorithm of the method.- 7.3 The stationary three-level method.- 7.3.1 The choice of the iterative parameters.- 7.3.2 An estimate for the rate of convergence.- 7.4 The stability of two-level and three-level methods relative to a priori data.- 7.4.1 Statement of the problem.- 7.4.2 Estimates for the convergence rates of the methods.- 8 Iterative Methods of Variational Type.- 8.1 Two-level gradient methods.- 8.1.1 The choice of the iterative parameters.- 8.1.2 A formula for the iterative parameters.- 8.1.3 An estimate of the convergence rate.- 8.1.4 Optimality of the estimate in the self-adjoint case.- 8.1.5 An asymptotic property of the gradient methods in the self-adjoint case.- 8.2 Examples of two-level gradient methods.- 8.2.1 The steepest-descent method.- 8.2.2 The minimal residual method.- 8.2.3 The minimal correction method.- 8.2.4 The minimal error method.- 8.2.5 A sample application of two-level methods.- 8.3 Three-level conjugate-direction methods.- 8.3.1 The choice of the iterative parameters. An estimate of the convergence rate.- 8.3.2 Formulas for the iterative parameters. The three-level iterative scheme.- 8.3.3 Variants of the computational formulas.- 8.4 Examples of the three-level methods.- 8.4.1 Special cases of the conjugate-direction methods.- 8.4.2 Locally optimal three-level methods.- 8.…