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
Notes on Optimizationby Pravin Varaiya - Van Nostrand
The author presents the main concepts mathematical programming and optimal control to students having diverse technical backgrounds. A reasonable knowledge of advanced calculus, linear algebra, and linear differential equations is required.
(12188 views)
Optimization Models For Decision Makingby Katta G. Murty - Springer
This is a Junior level book on some versatile optimization models for decision making in common use. The aim of this book is to develop skills in mathematical modeling, and in algorithms and computational methods to solve and analyze these models.
(11707 views)
Convex Optimization: Algorithms and Complexityby Sebastien Bubeck - arXiv.org
This text presents the main complexity theorems in convex optimization and their algorithms. Starting from the fundamental theory of black-box optimization, the material progresses towards recent advances in structural and stochastic optimization.
(6681 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.
(11200 views)