Data Assimilation: A Mathematical Introduction
by K.J.H. Law, A.M. Stuart, K.C. Zygalakis
Publisher: arXiv.org 2015
Number of pages: 158
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.
Home page url
Download or read it online for free here:
by 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.
by Dariush Khezrimotlagh - arXiv
I wrote this book as a self-teaching tool to assist every teacher, student, mathematician or non-mathematician, and to support their understanding of the elementary concepts on assessing the performance of a set of homogenous firms ...
by 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.
by Marius Durea, Radu Strugariu - De Gruyter Open
Starting with the case of differentiable data and the classical results on constrained optimization problems, continuing with the topic of nonsmooth objects involved in optimization, the book concentrates on both theoretical and practical aspects.