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Conjugate Gradient Type Methods for Ill-Posed Problems

The conjugate gradient method is a powerful tool for the iterative solution of self-adjoint operator equations in Hilbert space.This volume;

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Conjugate Gradient Type Methods for Ill-Posed Problems

The conjugate gradient method is a powerful tool for the iterative solution of self-adjoint operator equations in Hilbert space.This volume;

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Nonlinear Conjugate Gradient Methods for Unconstrained Optimization

of conjugate gradient algorithms for solving a collection of 800 unconstrained optimization problems of different structures and complexities with the;

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Nonlinear Conjugate Gradient Methods for Unconstrained Optimization

of conjugate gradient algorithms for solving a collection of 800 unconstrained optimization problems of different structures and complexities with the;

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Optimal Methods for Ill-Posed Problems

The book covers fundamentals of the theory of optimal methods for solving ill-posed problems, as well as ways to obtain accurate and;

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Dynamical Systems Method and Applications

, well-posed or ill-posed. The book presents a systematic development of the DSM, and theoretical results are illustrated by a number;

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Numerical Methods of Solving Ill-Posed Problems of Dielectric Spectrometry

Numerical Methods of Solving Ill-Posed Problems of Dielectric Spectrometry;

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Matrices, Moments and Quadrature with Applications

in iterative methods for solving linear systems and computing parameters in least squares and total least squares; and solving ill-posed problems using;

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Regularization Algorithms for Ill-Posed Problems

physics. The book presents in detail stable solution methods for ill-posed problems using the methodology of iterative regularization of classical;

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Introduction to Unconstrained Optimization with R

The book highlights methods such as the steepest descent method, Newton method, conjugate direction method, conjugate gradient methods;

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Regularization Methods for Ill-Posed Optimal Control Problems

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Applied Iterative Methods

iterative procedures. Topics include polynomial acceleration of basic iterative methods, Chebyshev and conjugate gradient acceleration procedures;

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An Introduction to Iterative Toeplitz Solvers

processing. This practical book introduces current developments in using iterative methods for solving Toeplitz systems based on the preconditioned;

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Inverse and Ill-posed Problems

information about its solution. Inverse problems were first encountered in practice and are mostly ill-posed. The urgent need for their solution;

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Abstract Cauchy Problems

Although the theory of well-posed Cauchy problems is reasonably understood, ill-posed problems-involved in a numerous mathematical models;

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Abstract Cauchy Problems

Although the theory of well-posed Cauchy problems is reasonably understood, ill-posed problems-involved in a numerous mathematical models;

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Classical and Modern Engineering Methods in Fluid Flow and Heat Transfer

of governing equations--for example in understanding the movement of heat across the boundary from one object to another or the transfer of energy from;

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Ill-Posed Problems in Probability & Stability of Random Sums

This volume is concerned with the problems in probability and statistics. Ill-posed problems are usually understood as those results where;

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Rank-Deficient and Discrete Ill-Posed Problems

for the analysis and solution of rank-deficient and discrete ill-posed problems. The emphasis is on insight into the stabilizing properties;

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Introduction to Unconstrained Optimization with R

, conjugate gradient methods, quasi-Newton methods, rank one correction formula, DFP method, BFGS method and their algorithms, convergence analysis;

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Riemannian Optimization and Its Applications

This brief describes the basics of Riemannian optimization-optimization on Riemannian manifolds-introduces algorithms for Riemannian;

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Inverse Problems and Applications

analytical developments and numerical methods for solutions of inverse and ill-posed problems, which consistently pose complex challenges to the;

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Gradient Type Methods for Large Scale Optimization

class of monotone gradient methods with globally convergence. Numerical results suggest that for non-quadratic minimization problem, the new;

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Numerical Solution of Partial Differential Equations

methods for simple but representative examples of parabolic, hyperbolic and elliptic equations from the first edition. However this is augmented;

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Numerical Solution of Partial Differential Equations

methods for simple but representative examples of parabolic, hyperbolic and elliptic equations from the first edition. However this is augmented;

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Optimization

gradient and stochastic methods as solution techniques for unconstrained and constrained optimization problems. It discusses the conjugate gradient;

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Inverse Problems in Physical Diagnostics

procedure for physicists, but, because there is no proportionality between data and solution errors in ill-posed problems, such approach;

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