Linear Optimisation and Numerical Analysis
by Ian Craw
Publisher: University of Aberdeen 2002
Number of pages: 151
The overall aim of the course is: to describe the simplex algorithm and show how it can be used to solve real problems; to show how previous results in linear algebra give a framework for understanding the simplex algorithm; and to place the simplex algorithm in a more general context by describing other calculus-based and computer based optimization algorithms.
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by Mark Embree - Rice University
This course takes a tour through many algorithms of numerical analysis. We aim to assess alternative methods based on efficiency, to discern well-posed problems from ill-posed ones, and to see these methods in action through computer implementation.
by C.T. Kelley - SIAM
This book focuses on a small number of methods and treats them in depth. The author provides a complete analysis of the conjugate gradient and generalized minimum residual iterations as well as recent advances including Newton-Krylov methods.
by K. Nandakumar - University of Alberta
Contents: On mathematical models; Single nonlinear algebraic equation; System of linear and nonlinear algebraic equations; Numerical differentiation and integration; Ordinary differential equations; Boundary value problems; etc.
by Jeffrey R. Chasnov - The Hong Kong University
This is primarily for non-mathematics majors and is required by several engineering departments. Contents: IEEE Arithmetic; Root Finding; Systems of equations; Least-squares approximation; Interpolation; Integration; Ordinary differential equations.