C++ for Scientists - Technische Universität Dresden

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208 CHAPTER 7. EFFECTIVE PROGRAMMING: THE POLYMORPHIC WAY Behind the Dynamic Polymorphism in C++ A programmer must be aware of the fact, that inheritance is one of the strongest bonds between objects. In real-world examples, few problems can be modeled successfully by class-inheritance only. The coupling by inheritance should be used very carefully. The advantages of this paradigm are: • Library: Data-types can be enhanced greatly. • Abstract algorithms with polymorphism enable greater code reusability compared to the procedural paradigm. • Strong binding of data structures and methods: Logical connections can be modeled easily. Logical errors can be detected easily. The disadvantages of this paradigm are: • The binary-method problem (see Section 7.5.3) • Bad optimization capability of a compiler due to the subtyping polymorphism (see Section ??) • Strong binding of data structures and methods: Only usable on object-oriented problems.
7.4. FUNCTIONAL PROGRAMMING 209 7.4 Functional Programming In contrast to the procedural and object oriented paradigm, which explicitly formulate algorithms and programs as a sequence of instructions which act on a program state, the functional paradigm uses mathematical functions for this task and forgoes the use of a state altogether. Therefore, there are no mutable variable and no side effects in purely functional programming. As such it is declarative in nature and relies on the language’s environment to produce an imperative representation which can be run on a physical machine. Among the greatest strengths of the functional paradigm is the availability of a strong theoretical framework of lambda calculus (cite()), which is explained in more detail in Section ref(), for the different implementations. Higher-order functions are an important concept of functional programming due to its usability in procedural languages. They were studied in lambda calculus theory well before the notion of functional programming existed and present the design of a number of functional programming languages, such as Scheme and Haskell. As modern procedural languages and their implementations have started to put greater emphasis on correctness, rather than raw speed, and the implementations of functional languages have begun to emphasize speed as well as correctness, the performance of functional languages and procedural languages has begun to converge. For programs which spend most of their time doing numerical computations, some functional languages (such as OCaml and Clean) can approach the performance of programs written in C speed, while for programs that handle large matrices and multidimensional databases, array functional languages (such as J and K) are usually faster than most non-optimized C programs. Functional languages have long been criticized as resource-hungry, both in terms of CPU resources and memory. This was mainly due to two things: • some early functional languages were implemented with no concern for efficiency • non-functional languages achieved speed at least in part by neglecting features such as checking of bounds or garbage collection which are viewed as essential parts of modern computing frameworks representing an overhead which was built-in to functional languages by default Since a purely functional description is free of side effects, it is a favourable choice for parallelization, as the description does not contain a state, which would require synchronization. Data related dependencies, however, must still be considered in order to ensure correct operation. Since the declarative style connected to the functional paradigm distances itself from the traditional imperative paradigm and its connection to states, input and output opearions pose a hurdle which is often addressed in a manner, which is not purely functional. As such functional interdependencies may be specified trivially, while the details how these are to be met remain opaque and as a choice to the specificc implementaiton. Last, we give an example of pure functional programming. We point out, that the next code snippet is presented in Haskel, not in C ++ syntax. The ”hello world” program in the functional programming paradigm: the factorial calculation fac :: Integer → Integer fac 0 = 1 fac n | n>0 = n ∗ fac (n−1)
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Technische Universität Dresden Fak
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Contents I Understanding C++ 7 Intr
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CONTENTS 5 10.5 Unix and Linux . .
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Part I Understanding C ++ 7
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10 C ++ was not a reliable computer
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12 CHAPTER 1. GOOD AND BAD SCIENTIF
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20 CHAPTER 2. C++ BASICS • std::c
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22 CHAPTER 2. C++ BASICS In the fir
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24 CHAPTER 2. C++ BASICS int main (
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26 CHAPTER 2. C++ BASICS Operator A
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28 CHAPTER 2. C++ BASICS The bitwis
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30 CHAPTER 2. C++ BASICS cast (type
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32 CHAPTER 2. C++ BASICS complicate
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34 CHAPTER 2. C++ BASICS } else if
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36 CHAPTER 2. C++ BASICS eps/= 2.0;
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38 CHAPTER 2. C++ BASICS for (...;
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40 CHAPTER 2. C++ BASICS 2.6.1 Inli
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42 CHAPTER 2. C++ BASICS To make su
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44 CHAPTER 2. C++ BASICS float divi
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46 CHAPTER 2. C++ BASICS The first
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48 CHAPTER 2. C++ BASICS #ifndef at
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50 CHAPTER 2. C++ BASICS float A[7]
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52 CHAPTER 2. C++ BASICS Encapsulat
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54 CHAPTER 2. C++ BASICS As a pract
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56 CHAPTER 2. C++ BASICS A function
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58 CHAPTER 2. C++ BASICS Now that t
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60 CHAPTER 2. C++ BASICS we need sm
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62 CHAPTER 2. C++ BASICS 2.12 Exerc
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64 CHAPTER 2. C++ BASICS 2.13 Opera
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66 CHAPTER 3. CLASSES apply symm bl
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68 CHAPTER 3. CLASSES int main() {
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70 CHAPTER 3. CLASSES class solver
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72 CHAPTER 3. CLASSES • Compilers
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74 CHAPTER 3. CLASSES a real number
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76 CHAPTER 3. CLASSES } return ∗t
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78 CHAPTER 3. CLASSES This mechanis
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80 CHAPTER 3. CLASSES One could not
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82 CHAPTER 3. CLASSES class matrix
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84 CHAPTER 3. CLASSES Approach 3: R
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86 CHAPTER 3. CLASSES of the decrem
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88 CHAPTER 3. CLASSES There are two
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90 CHAPTER 4. GENERIC PROGRAMMING }
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92 CHAPTER 4. GENERIC PROGRAMMING c
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94 CHAPTER 4. GENERIC PROGRAMMING A
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96 CHAPTER 4. GENERIC PROGRAMMING v
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98 CHAPTER 4. GENERIC PROGRAMMING c
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100 CHAPTER 4. GENERIC PROGRAMMING
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134 CHAPTER 5. META-PROGRAMMING exp
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138 CHAPTER 5. META-PROGRAMMING We
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140 CHAPTER 5. META-PROGRAMMING Fir
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146 CHAPTER 5. META-PROGRAMMING tra
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150 CHAPTER 5. META-PROGRAMMING 5.3
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152 CHAPTER 5. META-PROGRAMMING •
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154 CHAPTER 5. META-PROGRAMMING Dis
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156 CHAPTER 5. META-PROGRAMMING };
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Parallelism Chapter 13 13.1 Multi-T
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13.2. MESSAGE PASSING 261 int main
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Numerical exercises Chapter 14 In t
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14.1. COMPUTING AN EIGENFUNCTION OF
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14.3. THE SOLUTION OF A SYSTEM OF D
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14.4. GOOGLE’S PAGE RANK 275 Taki
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14.5. THE BISECTION METHOD FOR FIND
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14.6. THE NEWTON-RAPHSON METHOD FOR
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14.7. SEQUENTIAL NOISE REDUCTION OF
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14.7. SEQUENTIAL NOISE REDUCTION OF
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Programmierprojekte Kapitel 15 Die
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15.6. MATRIX-SKALIERUNG 287 Siehe h
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15.10. ANWENDUNG MTL4 AUF TYPEN MIT
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Acknowledgement Chapter 16 Special
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Bibliography [AG04] David Abrahams
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