Practical Mathematical Optimization 2ed (2018, Springer)

Practical Mathematical Optimization_ Basic Optimization Theory and GradientBased Algorithms (2018, Springer) 斯普林格 优化系列图书，2018年2ed ，英文，清晰带目录
 16.36MB
Tom Apostol Mathematical Analysis 2ed.pdf
20180911Tom Apostol Mathematical Analysis 2ed.pdf
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mathematical optimization and economic theory
20150317mathematical optimization and economic theory
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Practical Python AI Projects Mathematical Models of Optimization Problems epub
20180305Practical Python AI Projects Mathematical Models of Optimization Problems with Google ORTools 英文epub 本资源转载自网络，如有侵权，请联系上传者或csdn删除 查看此书详细信息请在美国亚马逊官网搜索此书
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A Mathematical Introduction to Compressive Sensing Springer 2013
20131014A Mathematical Introduction to Compressive Sensing Springer 2013 Authors:Simon Foucart, Holger Rauhut
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Mathematical Optimization in Computer Graphics and Vision 无水印pdf
20170924Mathematical Optimization in Computer Graphics and Vision 英文无水印pdf pdf所有页面使用FoxitReader和PDFXChangeViewer测试都可以打开 本资源转载自网络，如有侵权，请联系上传者或csdn删除 本资源转载自网络，如有侵权，请联系上传者或csdn删除
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[Andreas Antoniou]Practical Optimization.pdf
20090912Dedication v Biographies of the authors vii Preface xv Abbreviations xix 1. THE OPTIMIZATION PROBLEM 1 1.1 Introduction 1 1.2 The Basic Optimization Problem 4 1.3 General Structure of Optimization Algorithms 8 1.4 Constraints 10 1.5 The Feasible Region 17 1.6 Branches of Mathematical Programming 22
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Mathematical Optimization of Water Networks
20190319© Springer Basel 2012 This work is subject to copyright. All rights are reserved by the Publisher, whether the whole or part of the material is concerned, speciﬁcally the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microﬁlms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. Exempted from this legal reservation are brief excerpts in connection with reviews or scholarly analysis or material supplied speciﬁcally for the purpose of being entered and executed on a computer system, for exclusive use by the purchaser of the work. Duplication of this publication or parts thereof is permitted only under the provisions of the Copyright Law of the Publisher’s location, in its current version, and permission for use must always be obtained from Springer. Permissions for use may be obtained through RightsLink at the Copyright Clearance Center. Violations are liable to prosecution under the respective Copyright Law. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a speciﬁc statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. While the advice and information in this book are believed to be true and accurate at the date of publication, neither the authors nor the editors nor the publisher can accept any legal responsibility for any errors or omissions that may be made. The publisher makes no warranty, express or implied, with respect to the material contained herein.
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Mathematical Modeling 4ed
20141129Mathematical Modeling, Fourth EditionAcademic Press(2013)
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Philosophical and Mathematical Logic (Springer原版超清)
20190209Philosophical and Mathematical Logic (Springer Undergraduate Texts in Philosophy) By 作者: Harrie de Swart ISBN10 书号: 3030032531 ISBN13 书号: 9783030032531 Edition 版本: 1st ed. 2018 Release Finelybook 出版日期: 20181128 pages 页数: (539 ) $49.99 This book was written to serve as an introduction to logic, with in each chapter – if applicable – special emphasis on the interplay between logic and philosophy, mathematics, language and (theoretical) computer science. The reader will not only be provided with an introduction to classical logic, but to philosophical (modal, epistemic, deontic, temporal) and intuitionistic logic as well. The first chapter is an easy to read nontechnical Introduction to the topics in the book. The next chapters are consecutively about Propositional Logic, Sets (finite and infinite), Predicate Logic, Arithmetic and Gödel’s Incompleteness Theorems, Modal Logic, Philosophy of Language, Intuitionism and Intuitionistic Logic, Applications (Prolog; Relational Databases and SQL; Social Choice Theory, in particular Majority Judgment) and finally, Fallacies and Unfair Discussion Methods. Throughout the text, the author provides some impressions of the historical development of logic: Stoic and Aristotelian logic, logic in the Middle Ages and Frege’s Begriffsschrift, together with the works of George Boole (18151864) and August De Morgan (18061871), the origin of modern logic. Since “if …, then …” can be considered to be the heart of logic, throughout this book much attention is paid to conditionals: material, strict and relevant implication, entailment, counterfactuals and conversational implicature are treated and many references for further reading are given. Each chapter is concluded with answers to the exercises.
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Mathematical Statistics (Jun Shao)
19700109基于测度论的概率统计教材，数学专业。作者Jun Shao。
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Tom Apostol  Mathematical Analysis 2ed
20090804improve the mathanalytical conception; boring & tiresome
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Practical Python AI Projects2018年
20180228Python人工只能工程实践，值得一睹。Solving artificial intelligence problems with Python using optimization modeling
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Numerical Optimization 2ed  Jorge Nocedal
20141030Optimization is an important tool used in decision science and for the analysis of physical systems used in engineering. One can trace its roots to the Calculus of Variations and the work of Euler and Lagrange. This natural and reasonable approach to mathematical programming covers numerical methods for finitedimensional optimization problems. It begins with very simple ideas progressing through more complicated concepts, concentrating on methods for both unconstrained and constrained optimization.
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Mathematical Optimization for Computer Graphics and vision
20120225一本对计算机图形和计算机视觉算法进行数学上的优化的一本书，英文版。MORGAN KAUFMANN系列，作者Luiz Velho, Paulo Cezar Pinto Carvalho, Luiz Henrique de Figueiredo, Jonas Gomes.
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Deep Neural Networks in a Mathematical FrameworkSpringer(2018).pdf
20180327Over the past decade, Deep Neural Networks (DNNs) have become very popular models for problems involving massive amounts of data. The most successful DNNs tend to be characterized by several layers of parametrized linear and nonlinear transformations, such that the model contains an immense number o
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mathematical statistics and data analysis 2ed (djvu)
20100511统计书籍：mathematical statistics and data analysis 2ed (djvu version)
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Sontag_Mathematical_Control_Theory_Springer98
20110512This textbook introduces the core concepts and results of Control and System Theory. Unique in its emphasis on foundational aspects, it takes a "hybrid" approach in which basic results are derived for discrete and continuous time scales, and discrete and continuous state variables. Primarily geared towards mathematically advanced undergraduate or graduate students, it may also be suitable for a second engineering course in control which goes beyond the classical frequency domain and statespace material. The choice of topics, together with detailed endofchapter links to the bibliography, makes it an excellent research reference as well.
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Convex Optimization
20091020最权威实用的工程优化书籍，英文原著 This book is about convex optimization, a special class of mathematical optimization problems, which includes leastsquares and linear programming problems. It is well known that leastsquares and linear programming problems have a fairly complete theory, arise in a variety of applications, and can be solved numerically very e±ciently. The basic point of this book is that the same can be said for the larger class of convex optimization problems. While the mathematics of convex optimization has been studied for about a century, several related recent developments have stimulated new interest in the topic. The ¯rst is the recognition that interiorpoint methods, developed in the 1980s to solve linear programming problems, can be used to solve convex optimization problems as well. These new methods allow us to solve certain new classes of convex optimization problems, such as semide¯nite programs and secondorder cone programs, almost as easily as linear programs.

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