Data Assimilation: A Mathematical Introduction
by K.J.H. Law, A.M. Stuart, K.C. Zygalakis
Publisher: arXiv.org 2015
ISBN-13: 9783319203256
Number of pages: 158
Description:
This book provides a systematic treatment of the mathematical underpinnings of work in data assimilation, covering both theoretical and computational approaches. Specifically the authors develop a unified mathematical framework in which a Bayesian formulation of the problem provides the bedrock for the derivation, development and analysis of algorithms; the many examples used in the text, together with the algorithms which are introduced and discussed, are all illustrated by the MATLAB software detailed in the book and made freely available online.
Download or read it online for free here:
Download link
(2.2MB, PDF)
Similar books
Optimization and Dynamical Systemsby U. Helmke, J. B. Moore - Springer
Aimed at mathematics and engineering graduate students and researchers in the areas of optimization, dynamical systems, control systems, signal processing, and linear algebra. The problems solved are those of linear algebra and linear systems theory.
(13313 views)
The Design of Approximation Algorithmsby D. P. Williamson, D. B. Shmoys - Cambridge University Press
This book shows how to design approximation algorithms: efficient algorithms that find provably near-optimal solutions. It is organized around techniques for designing approximation algorithms, including greedy and local search algorithms.
(14890 views)
Discrete Optimizationby Guido Schaefer - Utrecht University
From the table of contents: Preliminaries (Optimization Problems); Minimum Spanning Trees; Matroids; Shortest Paths; Maximum Flows; Minimum Cost Flows; Matchings; Integrality of Polyhedra; Complexity Theory; Approximation Algorithms.
(8211 views)
Iterative Methods for Optimizationby C.T. Kelley - Society for Industrial Mathematics
This book presents a carefully selected group of methods for unconstrained and bound constrained optimization problems and analyzes them in depth both theoretically and algorithmically. It focuses on clarity in algorithmic description and analysis.
(10019 views)