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Concepts in Programming Languages.pdf
Concepts in Programming Languages by John Mitchell.一本国外经典教材，看了之后对编程语言更加理解。费了很多劲才找到的。Concepts in Programming LanguagesThis textbook for undergraduate and beginning graduate students explains and examines the central concepts used in modern programminglanguages, such as functions, types, memory management, and controlThe book is unique in its comprehensive presentation and comparisonof major object-oriented programming languages. Separate chapters ex-amine the history of objects, Simula and Smalltalk, and the prominentanguages c++ and JavaThe author presents foundational topics, such as lambda calculus anddenotational semantics, in an easy-to-read, informal style, focusing on themain insights provided by these theories. Advanced topics include concurrency and concurrent object-oriented programming. A chapter on logicprogramming illustrates the importance of specialized programming meth-ods for certain kinds of problemsThis book will give the reader a better understanding of the issuesand trade-offs that arise in programming language design and a betterappreciation of the advantages and pitfalls of the programming languagesthey useJohn C. mitchell is Professor of Computer Science at Stanford University,where he has been a popular teacher for more than a decade. Many of hisformer students are successful in research and private industry. He received his ph D. from mit in 1984 and was a member of technical staff atat&T Bell Laboratories before joining the faculty at Stanford. Over thepast twenty years, Mitchell has been a featured speaker at internationalconferences; has led research projects on a variety of topics, includingprogramming language design and analysis, computer security, and applications of mathematical logic to computer science; and has written morethan 100 research articles. His previous textbook, Foundations for Pro-gramming Languages(MIT Press, 1996), covers lambda calculus, typesystems, logic for program verification, and mathematical semantics ofprogramming languages. Professor Mitchell was a member of the programming language subcommittee of the ACM/ieEE Curriculum 2001standardization effort and the 2002 Program Chair of the aCm principlesof programming languages conferenceCONCEPTS NPROGRAMMINGLANGUAGESJohn c. mitchellStanford UniversityCAMBRIDGEUNIVERSITY PRESSPUBLISHED BY THE PRESS SYNDICATE OF THE UNIVERSITY OF CAMBRIDGEThe Pitt Building, Trumpington Street, Cambridge, United KingdomCAMBRIDGE UNIVERSITY PRESSThe Edinburgh Building, Cambridge CB2 2RU, UK40 West 20th Street, New York, NY 10011-4211 USA477 Williamstown Road, Port Melbourne vic 3207, AustraliaRuiz de alarcon 13, 28014 Madrid, spainDock House, The Waterfront, Cape Town 8001, South Africahttp://www.cambridge.orgo Cambridge university press 2004First published in printed format 2002isBN 0-511-03492-X eBook(adobe readerISBN 0-521-78098-5 hardbackContentsPrefacepage IxPart 1 functions and foundations1 Introduction1.1 Programming Languages1.2 Goals1.3 Programming Language History3561.4 Organization: Concepts and Languages2 Computability2. 1 Partial Functions and computability102.2 Chapter SummaryExercises163 Lisp: Functions, Recursion, and Lists3.1 Lisp History183.2 Good Language design203. 3 Brief Language overview223.4 Innovations in the Design of Lisp253.5 Chapter Summary: Contributions of LispExercises404 Fundamentals484.1 Compilers and syntax484.2 Lambda calculus4.3 Denotational semantics4.4 Functional and Imperative Languages4.5 Chapter SummaryExercisesContentsPart 2 Procedures, Types, Memory Management, and Control5 The algol Family and ML5.1 The Algol Family of Programming Languages5.2 The Development of C5.3 The LCF System and ml5.4 The Ml Programming Language1035.5 Chapter summary121Exercises1226 Type Systems and Type Inference1296.1 Types in Programming1296.2 Type Safety and Type Checking1326.3 Type Inference1356.4 Polymorphism and Overloadin1456.5 Type Declarations and Type Equality1516.6 Chapter Summary155Exercises1567 Scope, Functions, and storage Management1627.1 Block-Structured Languages1627.2 In-Line blocks1657.3 Functions and procedures1707.4 Higher-Order functions1827.5 Chapter summary190Exercises1918 Control in Sequential Languages2048.1 Structured control2048.2 Exceptions2078.3 Continuations2188.4 Functions and evaluation order2238.5 Chapter summary227Exercises8Part 3 Modularity, Abstraction, and object-Oriented Programming9 Data Abstraction and Modularity2359.1 Structured Programming2359.2 Language Support for Abstraction2429.3 Modules9.4 Generic Abstractions2599.5 Chapter Summary269Exercises27110 Concepts in Object-Oriented Languages27710.1 Object-Oriented design27710.2 Four Basic concepts in object-Oriented languages278Contents10.3 Program Structure28810.4 Design Patterns29010.5 Chapter summary29210.6 Looking Forward: Simula, SmalltalkC++Java293Exercises29411 History of objects: Simula and smalltalk30011.1 Origin of Objects in Simula30011.2 Objects in Simula30311.3 Subclasses and Subtypes in Simula30811.4 Development of smalltalk31011.5 Smalltalk Language features31211.6 Smalltalk flexibilit31811.7 Relationship between Subtyping andInheritance2211.8 Chapter SummaryExercises32712 objects and Run-Time Efficiency: C++33712.1 Design goals and Constraints33712.2 Overview of c++34012.3 Classes. Inheritance and Virtual functions34612.4 Subtyping35512.5 Multiple inheritance12.6 Chapter summary366Exercises36713 Portability and Safety: Java38413.1 Java language overview38613.2 Java Classes and Inheritance38913.3 Java Types and Subtyping39613.4 Java System architecture40413.5 Security Features41213.6 Java summary417Exercises420Part 4 Concurrency and Logic Programming14 Concurrent and Distributed Programming43114.1 Basic Concepts in Concurrency43314.2 The actor model44114.3 Concurrent ML14.4 Java concurrency45414.5 Chapter Summary466Exercises469Contents15 The Logic Programming Paradigm and Prolog47515. 1 History of logic Programming15.2 Brief Overview of the logic Programming Paradigm4715. 3 Equations solved by Unification as Atomic Actions15.4 Clauses as Parts of procedure declarations48215.5 Prologs Approach to Programming48615.6 Arithmetic in Prolog49215.7 Control, Ambivalent Syntax, and Meta-Variables49615.8 Assessment of Prolog50515.9 Bibliographic remarks50715.10 Chapter Summary507Appendix a Additional Program Examples509A 1 Procedural and Object-Oriented organization509Glossary521Index525
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多幅图像拼接matlab实现 源码下载
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LogViewPro中文版 超大文本文件打开工具
LogViewPro中文版 超大文本文件打开利器 不论多少G的文本文件 全部秒开，秒杀ultraedit 等一切自称能够秒开的程序。
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用ADS进行RF射频系统模拟
本文采用超外差式接收机信号经天线进入接收机首先经过射频低噪声放大器放大，经由射频滤波器抑制镜像干扰和其他杂散信号，再与本振相混频，变换成中频信号，经过滤波放大得到中频输出。
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verilog HDL实现双线性插值视频缩放
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电力拖动自动控制系统---运动控制系统（上海大学 陈伯时 第三版） 书附全部课件
电力拖动自动控制系统 运动控制系统 上海大学 陈伯时 课件
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Inverse Synthetic Aperture Radar Imaging With MATLAB Algorithms
这本书提供了充分的代表性逆合成孔径雷达（ISAR）成像，这是一种流行和重要的雷达信号处理工具。本书涵盖了所有可能的ISAR成像方面。这本书提供了一个介绍了专家组的逆问题和合成孔径雷达（SAR）提出的问题之前，信号处理技术和雷达基本相当。如高分辨率SAR的重要概念，脉冲压缩和图像形成连同相关的MATLAB代码。 ISAR成像的基本原理后，这本书提供了相关的MATLAB函数和代码成像ISAR成像的详细程序。 ISAR成像，多个成像技巧和微调，如零填充和窗口程序，以提高图像的质量也被提出。最后，逆合成孔径雷达图像中的各种实际应用，如成像天线平台散射的，是在单独的一章。所有这些算法，MATLAB代码和
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凸优化在信号处理与通信中的应用Convex Optimization in Signal Processing and Communications
凸优化理论在信号处理以及通信系统中的应用 比较经典的通信系统凸优化入门教程ContentsList of contributorspage IxPrefaceAutomatic code generation for real- time convex optimizationJacob Mattingley and stephen Boyd1.1 Introduction1.2 Solvers and specification languages61. 3 Examples121. 4 Algorithm considerations1.5 Code generation261.6 CVXMOD: a preliminary implementation281.7 Numerical examples291. 8 Summary, conclusions, and implicationsAcknowledgments35ReferencesGradient-based algorithms with applications to signal-recoveryproblemsAmir beck and marc teboulle2.1 Introduction422.2 The general optimization model432.3 Building gradient-based schemes462. 4 Convergence results for the proximal-gradient method2.5 A fast proximal-gradient method2.6 Algorithms for l1-based regularization problems672.7 TV-based restoration problems2. 8 The source-localization problem772.9 Bibliographic notes83References85ContentsGraphical models of autoregressive processes89Jitkomut Songsiri, Joachim Dahl, and Lieven Vandenberghe3.1 Introduction893.2 Autoregressive processes923.3 Autoregressive graphical models983. 4 Numerical examples1043.5 Conclusion113Acknowledgments114References114SDP relaxation of homogeneous quadratic optimization: approximationbounds and applicationsZhi-Quan Luo and Tsung-Hui Chang4.1 Introduction1174.2 Nonconvex QCQPs and sDP relaxation1184.3 SDP relaxation for separable homogeneous QCQPs1234.4 SDP relaxation for maximization homogeneous QCQPs1374.5 SDP relaxation for fractional QCQPs1434.6 More applications of SDP relaxation1564.7 Summary and discussion161Acknowledgments162References162Probabilistic analysis of semidefinite relaxation detectors for multiple-input,multiple-output systems166Anthony Man-Cho So and Yinyu Ye5.1 Introduction1665.2 Problem formulation1695.3 Analysis of the SDr detector for the MPsK constellations1725.4 Extension to the Qam constellations1795.5 Concluding remarks182Acknowledgments182References189Semidefinite programming matrix decomposition, and radar code design192Yongwei Huang, Antonio De Maio, and Shuzhong Zhang6.1 Introduction and notation1926.2 Matrix rank-1 decomposition1946.3 Semidefinite programming2006.4 Quadratically constrained quadratic programming andts sdp relaxation201Contents6.5 Polynomially solvable QCQP problems2036.6 The radar code-design problem2086.7 Performance measures for code design2116.8 Optimal code design2146.9 Performance analysis2186.10 Conclusions223References226Convex analysis for non-negative blind source separation withapplication in imaging22Wing-Kin Ma, Tsung-Han Chan, Chong-Yung Chi, and Yue Wang7.1 Introduction2297.2 Problem statement2317.3 Review of some concepts in convex analysis2367.4 Non-negative, blind source-Separation criterion via CAMNS2387.5 Systematic linear-programming method for CAMNS2457.6 Alternating volume-maximization heuristics for CAMNS2487.7 Numerical results2527.8 Summary and discussion257Acknowledgments263References263Optimization techniques in modern sampling theory266Tomer Michaeli and yonina c. eldar8.1 Introduction2668.2 Notation and mathematical preliminaries2688.3 Sampling and reconstruction setup2708.4 Optimization methods2788.5 Subspace priors2808.6 Smoothness priors2908.7 Comparison of the various scenarios3008.8 Sampling with noise3028. 9 Conclusions310Acknowledgments311References311Robust broadband adaptive beamforming using convex optimizationMichael Rubsamen, Amr El-Keyi, Alex B Gershman, and Thia Kirubarajan9.1 Introduction3159.2 Background3179.3 Robust broadband beamformers3219.4 Simulations330Contents9.5 Conclusions337Acknowledgments337References337Cooperative distributed multi-agent optimization340Angelia Nedic and asuman ozdaglar10.1 Introduction and motivation34010.2 Distributed-optimization methods using dual decomposition34310.3 Distributed-optimization methods using consensus algorithms35810.4 Extensions37210.5 Future work37810.6 Conclusions38010.7 Problems381References384Competitive optimization of cognitive radio MIMO systems via game theory387Gesualso Scutari, Daniel P Palomar, and Sergio Barbarossa11.1 Introduction and motivation38711.2 Strategic non-cooperative games: basic solution concepts and algorithms 39311.3 Opportunistic communications over unlicensed bands411.4 Opportunistic communications under individual-interferenceconstraints4151.5 Opportunistic communications under global-interference constraints43111.6 Conclusions438Ackgment439References43912Nash equilibria: the variational approach443Francisco Facchinei and Jong-Shi Pang12.1 Introduction44312.2 The Nash-equilibrium problem4412. 3 EXI45512.4 Uniqueness theory46612.5 Sensitivity analysis47212.6 Iterative algorithms47812.7 A communication game483Acknowledgments490References491Afterword494Index49ContributorsSergio BarbarossaYonina c, eldarUniversity of rome-La SapienzaTechnion-Israel Institute of TechnologyHaifaIsraelAmir beckTechnion-Israel instituteAmr El-Keyiof TechnologyAlexandra universityHaifEgyptIsraelFrancisco facchiniStephen boydUniversity of rome La sapienzaStanford UniversityRomeCaliforniaItalyUSAAlex b, gershmanTsung-Han ChanDarmstadt University of TechnologyNational Tsing Hua UniversityDarmstadtHsinchuGermanyTaiwanYongwei HuangTsung-Hui ChangHong Kong university of scienceNational Tsing Hua Universityand TechnologyHsinchuHong KongTaiwanThia KirubarajanChong-Yung chiMcMaster UniversityNational Tsing Hua UniversityHamilton ontarioHsinchuCanadaTaiwanZhi-Quan LuoJoachim dahlUniversity of minnesotaanybody Technology A/sMinneapolisDenmarkUSAList of contributorsWing-Kin MaMichael rebsamenChinese University of Hong KongDarmstadt UniversityHong KonTechnologyDarmstadtAntonio de maioGermanyUniversita degli studi di napoliFederico iiGesualdo scutariNaplesHong Kong University of Sciencealyand TechnologyHong KongJacob MattingleyAnthony Man-Cho SoStanford UniversityChinese University of Hong KongCaliforniaHong KongUSAJitkomut songsinTomer michaeliUniversity of californiaTechnion-Israel instituteLoS Angeles. CaliforniaogyUSAHaifaMarc teboulleTel-Aviv UniversityAngelia NedicTel-AvUniversity of Illinois atIsraelUrbana-ChampaignInoSLieven VandenbergheUSAUniversity of CaliforniaLos Angeles, CaliforniaUSAAsuman OzdaglarMassachusetts Institute of TechnologyYue WangBoston massachusettsVirginia Polytechnic InstituteUSAand State UniversityArlingtonDaniel p palomarUSAHong Kong University ofScience and TechnologyYinyu YeHong KongStanford UniversityCaliforniaong-Shi PangUSAUniversity of illinoisat Urbana-ChampaignShuzhong zhangIllinoisChinese university of Hong KongUSAHong KongPrefaceThe past two decades have witnessed the onset of a surge of research in optimization.This includes theoretical aspects, as well as algorithmic developments such as generalizations of interior-point methods to a rich class of convex-optimization problemsThe development of general-purpose software tools together with insight generated bythe underlying theory have substantially enlarged the set of engineering-design problemsthat can be reliably solved in an efficient manner. The engineering community has greatlybenefited from these recent advances to the point where convex optimization has nowemerged as a major signal-processing technique on the other hand, innovative applica-tions of convex optimization in signal processing combined with the need for robust andefficient methods that can operate in real time have motivated the optimization commu-nity to develop additional needed results and methods. The combined efforts in both theoptimization and signal-processing communities have led to technical breakthroughs ina wide variety of topics due to the use of convex optimization This includes solutions tonumerous problems previously considered intractable; recognizing and solving convex-optimization problems that arise in applications of interest; utilizing the theory of convexoptimization to characterize and gain insight into the optimal-solution structure and toderive performance bounds; formulating convex relaxations of difficult problems; anddeveloping general purpose or application-driven specific algorithms, including thosethat enable large-scale optimization by exploiting the problem structureThis book aims at providing the reader with a series of tutorials on a wide varietyof convex-optimization applications in signal processing and communications, writtenby worldwide leading experts, and contributing to the diffusion of these new developments within the signal-processing community. The goal is to introduce convexoptimization to a broad signal-processing community, provide insights into how convexoptimization can be used in a variety of different contexts, and showcase some notablesuccesses. The topics included are automatic code generation for real-time solvers, graphical models for autoregressive processes, gradient-based algorithms for signal-recoveryapplications, semidefinite programming(SDP)relaxation with worst-case approximationperformance, radar waveform design via SDP, blind non-negative source separation forimage processing, modern sampling theory, robust broadband beamforming techniquesdistributed multiagent optimization for networked systems, cognitive radio systems viagame theory, and the variational-inequality approach for Nash-equilibrium solutionsPrefaceThere are excellent textbooks that introduce nonlinear and convex optimization, providing the reader with all the basics on convex analysis, reformulation of optimizationproblems, algorithms, and a number of insightful engineering applications. This book istargeted at advanced graduate students, or advanced researchers that are already familiarwith the basics of convex optimization. It can be used as a textbook for an advanced graduate course emphasizing applications, or as a complement to an introductory textbookthat provides up-to-date applications in engineering. It can also be used for self-study tobecome acquainted with the state of-the-art in a wide variety of engineering topicsThis book contains 12 diverse chapters written by recognized leading experts worldwide, covering a large variety of topics. Due to the diverse nature of the book chaptersit is not possible to organize the book into thematic areas and each chapter should betreated independently of the others. a brief account of each chapter is given nextIn Chapter 1, Mattingley and Boyd elaborate on the concept of convex optimizationin real-time embedded systems and automatic code generation. As opposed to genericsolvers that work for general classes of problems, in real-time embedded optimization thesame optimization problem is solved many times, with different data, often with a hardreal-time deadline. Within this setup the authors propose an automatic code-generationsystem that can then be compiled to yield an extremely efficient custom solver for theproblem familyIn Chapter 2, Beck and Teboulle provide a unified view of gradient-based algorithmsfor possibly nonconvex and non-differentiable problems, with applications to signalrecovery. They start by rederiving the gradient method from several different perspectives and suggest a modification that overcomes the slow convergence of the algorithmThey then apply the developed framework to different image-processing problems suchas e1-based regularization, TV-based denoising, and Tv-based deblurring, as well ascommunication applications like source localizationIn Chapter 3, Songsiri, Dahl, and Vandenberghe consider graphical models for autore-gressive processes. They take a parametric approach for maximum-likelihood andmaximum-entropy estimation of autoregressive models with conditional independenceconstraints, which translates into a sparsity pattern on the inverse of the spectral-densitymatrix. These constraints turn out to be nonconvex. To treat them the authors proposea relaxation which in some cases is an exact reformulation of the original problem. Theproposed methodology allows the selection of graphical models by fitting autoregressiveprocesses to different topologies and is illustrated in different applicationsThe following three chapters deal with optimization problems closely related to SDPand relaxation techniquesIn Chapter 4, Luo and Chang consider the SDP relaxation for several classes ofquadratic-optimization problems such as separable quadratically constrained quadraticprograms(QCQPs)and fractional QCQPs, with applications in communications and signal processing. They identify cases for which the relaxation is tight as well as classes ofquadratic-optimization problems whose relaxation provides a guaranteed, finite worstcase approximation performance. Numerical simulations are carried out to assess theefficacy of the SDP-relaxation approach
- 2020-12-10下载
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