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This book focuses on iterative methods for solving the Cauchy problem associated with the Helmholtz equation, a significant inverse problem in acoustics and field measurements. It explores how to recover solutions in a domain when only partial boundary data is available. The work addresses the ill-posed nature of the problem, where small errors in data can lead to large errors in solutions. By introducing modifications such as an artificial interior boundary and using conjugate gradient type methods, the author Lydie Mpinganzima develops techniques to restore stability and improve convergence speed. The book is intended for mathematicians and engineers interested in numerical methods for partial differential equations, particularly the Helmholtz equation.
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Iterative methods (Mathematics) --- Linear systems. --- Itération (Mathématiques) --- Systèmes linéaires --- Itération (Mathématiques) --- Systèmes linéaires
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"This book covers iterative optimization methods that stems from inverse problems and related issues. The author presents the theoretical side of inverse methods and as a result ignores discrete problems, stochastic methods, and combinatorial optimization. The coverage moves from an introduction of auxiliary function methods to a discussion of several examples of auxiliary fixed (AF) point methods in optimization to consideration of related topics such as operator fixed point methods. A few problems have been scattered throughout the book so that it might be used in a special topics class on optimization at the graduate level"--
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This book presents comprehensive state-of-the-art theoretical analysis of the fundamental Newtonian and Newtonian-related approaches to solving optimization and variational problems. A central focus is the relationship between the basic Newton scheme for a given problem and algorithms that also enjoy fast local convergence. The authors develop general perturbed Newtonian frameworks that preserve fast convergence and consider specific algorithms as particular cases within those frameworks, i.e., as perturbations of the associated basic Newton iterations. This approach yields a set of tools for the unified treatment of various algorithms, including some not of the Newton type per se. Among the new subjects addressed is the class of degenerate problems. In particular, the phenomenon of attraction of Newton iterates to critical Lagrange multipliers and its consequences as well as stabilized Newton methods for variational problems and stabilized sequential quadratic programming for optimization. This volume will be useful to researchers and graduate students in the fields of optimization and variational analysis.
Calculus of variations. --- Mathematical optimization. --- Iterative methods (Mathematics) --- Optimization (Mathematics) --- Optimization techniques --- Optimization theory --- Systems optimization --- Mathematical analysis --- Maxima and minima --- Operations research --- Simulation methods --- System analysis --- Iteration (Mathematics) --- Numerical analysis --- Isoperimetrical problems --- Variations, Calculus of --- Operations Research, Management Science. --- Continuous Optimization. --- Optimization. --- Operations research. --- Management science. --- Quantitative business analysis --- Management --- Problem solving --- Statistical decision --- Operational analysis --- Operational research --- Industrial engineering --- Management science --- Research --- System theory
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