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算法设计与分析经典教材
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"algorithm design" 《Algorithm Design》是美国南加州大学(USC)算法课程推荐的一本经典教材,由著名计算机科学家Jon Kleinberg 和 Éva Tardos 合著。这本书以其深入浅出的讲解方式,深受全球学子喜爱,特别适合希望深入理解算法原理的读者。书中涵盖了算法设计的基本思想、方法和分析技巧,旨在帮助读者不仅掌握具体算法,还能学会如何设计和评估新的算法。 本书的内容结构严谨,涵盖了诸多重要算法主题,如图论、动态规划、网络流、贪心算法、近似算法以及随机化方法等。在每章中,作者都会首先介绍一个核心的算法设计范式,然后通过实例来展示如何应用这些范式解决问题。此外,书中还提供了大量的练习题和案例研究,以帮助读者巩固所学知识并提升问题解决能力。 作为一本国外经典教材,《Algorithm Design》强调了理论与实践的结合,它不只是一本教学用书,也是研究者和工程师的宝贵参考资料。书中涉及的算法在计算机科学和工程领域有着广泛的应用,包括数据挖掘、网络优化、物流调度、生物信息学等多个方面。 在编写上,该书的排版和插图都经过精心设计,使得复杂的算法概念能够清晰地呈现给读者。同时,专业的编辑和技术团队确保了内容的准确性和可读性。索引和参考文献部分则方便读者进一步深入研究特定主题。 《Algorithm Design》是一本全面而深入的算法教科书,无论你是计算机科学的学生、教师,还是在业界工作的专业人士,都能从中受益匪浅,提升自己在算法设计和分析方面的技能。通过阅读此书,你将不仅掌握一系列经典算法,还能培养出解决实际问题的能力,这对于在信息时代立足至关重要。
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xiv
Preface
problems out in the world. At their most effective, then, algorithmic ideas do
not just provide solutions to well-posed problems; they form the language that
lets you cleanly express the underlying questions.
The goal of our book is to convey this approach to algorithms, as a design
process that begins with problems arising across the full range of computing
applications, builds on an understanding of algorithm design techniques, and
results in the development of efficient solutions to these problems. We seek
to explore the role of algorithmic ideas in computer science generally, and
relate these ideas to the range of precisely formulated problems for which we
can design and analyze algorithms. In other words, what are the underlying
issues that motivate these problems, and how did we choose these particular
ways of formulating them? How did we recognize which design principles were
appropriate in different situations?
In keeping with this, our goal is to offer advice on how to identify clean
algorithmic problem formulations in complex issues from different areas of
computing and, from this, how to design efficient algorithms for the resulting
problems. Sophisticated algorithms are often best understood by reconstruct-
ing the sequence of ideas—including false starts and dead ends—that led from
simpler initial approaches to the eventual solution. The result is a style of ex-
position that does not take the most direct route from problem statement to
algorithm, but we feel it better reflects the way that we and our colleagues
genuinely think about these questions.
Overview
The book is intended for students who have completed a programming-
based two-semester introductory computer science sequence (the standard
“CS1/CS2” courses) in which they have written programs that implement
basic algorithms, manipulate discrete structures such as trees and graphs, and
apply basic data structures such as arrays, lists, queues, and stacks. Since
the interface between CS1/CS2 and a first algorithms course is not entirely
standard, we begin the book with self-contained coverage of topics that at
some institutions are familiar to students from CS1/CS2, but which at other
institutions are included in the syllabi of the first algorithms course. This
material can thus be treated either as a review or as new material; by including
it, we hope the book can be used in a broader array of courses, and with more
flexibility in the prerequisite knowledge that is assumed.
In keeping with the approach outlined above, we develop the basic algo-
rithm design techniques by drawing on problems from across many areas of
computer science and related fields. To mention a few representative examples
here, we include fairly detailed discussions of applications from systems and
networks (caching, switching, interdomain routing on the Internet), artificial
Preface
xv
intelligence (planning, game playing, Hopfield networks), computer vision
(image segmentation), data mining (change-point detection, clustering), op-
erations research (airline scheduling), and computational biology (sequence
alignment, RNA secondary structure).
The notion of computational intractability, and NP-completeness in par-
ticular, plays a large role in the book. This is consistent with how we think
about the overall process of algorithm design. Some of the time, an interest-
ing problem arising in an application area will be amenable to an efficient
solution, and some of the time it will be provably NP-complete; in order to
fully address a new algorithmic problem, one should be able to explore both
of these options with equal familiarity. Since so many natural problems in
computer science are NP-complete, the development of methods to deal with
intractable problems has become a crucial issue in the study of algorithms,
and our book heavily reflects this theme. The discovery that a problem is NP-
complete should not be taken as the end of the story, but as an invitation to
begin looking for approximation algorithms, heuristic local search techniques,
or tractable special cases. We include extensive coverage of each of these three
approaches.
Problems and Solved Exercises
An important feature of the book is the collection of problems. Across all
chapters, the book includes over 200 problems, almost all of them developed
and class-tested in homework or exams as part of our teaching of the course
at Cornell. We view the problems as a crucial component of the book, and
they are structured in keeping with our overall approach to the material. Most
of them consist of extended verbal descriptions of a problem arising in an
application area in computer science or elsewhere out in the world, and part of
the problem is to practice what we discuss in the text: setting up the necessary
notation and formalization, designing an algorithm, and then analyzing it and
proving it correct. (We view a complete answer to one of these problems as
consisting of all these components: a fully explained algorithm, an analysis of
the running time, and a proof of correctness.) The ideas for these problems
come in large part from discussions we have had over the years with people
working in different areas, and in some cases they serve the dual purpose of
recording an interesting (though manageable) application of algorithms that
we haven’t seen written down anywhere else.
To help with the process of working on these problems, we include in
each chapter a section entitled “Solved Exercises,” where we take one or more
problems and describe how to go about formulating a solution. The discussion
devoted to each solved exercise is therefore significantly longer than what
would be needed simply to write a complete, correct solution (in other words,
xvi
Preface
significantly longer than what it would take to receive full credit if these were
being assigned as homework problems). Rather, as with the rest of the text,
the discussions in these sections should be viewed as trying to give a sense
of the larger process by which one might think about problems of this type,
culminating in the specification of a precise solution.
It is worth mentioning two points concerning the use of these problems
as homework in a course. First, the problems are sequenced roughly in order
of increasing difficulty, but this is only an approximate guide and we advise
against placing too much weight on it: since the bulk of the problems were
designed as homework for our undergraduate class, large subsets of the
problems in each chapter are really closely comparable in terms of difficulty.
Second, aside from the lowest-numbered ones, the problems are designed to
involve some investment of time, both to relate the problem description to the
algorithmic techniques in the chapter, and then to actually design the necessary
algorithm. In our undergraduate class, we have tended to assign roughly three
of these problems per week.
Pedagogical Features and Supplements
In addition to the problems and solved exercises, the book has a number of
further pedagogical features, as well as additional supplements to facilitate its
use for teaching.
As noted earlier, a large number of the sections in the book are devoted
to the formulation of an algorithmic problem—including its background and
underlying motivation—and the design and analysis of an algorithm for this
problem. To reflect this style, these sections are consistently structured around
a sequence of subsections: “The Problem,” where the problem is described
and a precise formulation is worked out; “Designing the Algorithm,” where
the appropriate design technique is employed to develop an algorithm; and
“Analyzing the Algorithm,” which proves properties of the algorithm and
analyzes its efficiency. These subsections are highlighted in the text with an
icon depicting a feather. In cases where extensions to the problem or further
analysis of the algorithm is pursued, there are additional subsections devoted
to these issues. The goal of this structure is to offer a relatively uniform style
of presentation that moves from the initial discussion of a problem arising in a
computing application through to the detailed analysis of a method to solve it.
A number of supplements are available in support of the book itself. An
instructor’s manual works through all the problems, providing full solutions to
each. A set of lecture slides, developed by Kevin Wayne of Princeton University,
is also available; these slides follow the order of the book’s sections and can
thus be used as the foundation for lectures in a course based on the book. These
files are available at www.aw.com. For instructions on obtaining a professor
Preface
xvii
login and password, search the site for either “Kleinberg” or “Tardos” or
contact your local Addison-Wesley representative.
Finally, we would appreciate receiving feedback on the book. In particular,
as in any book of this length, there are undoubtedly errors that have remained
in the final version. Comments and reports of errors can be sent to us by e-mail,
at the address algbook@cs.cornell.edu; please include the word “feedback”
in the subject line of the message.
Chapter-by-Chapter Synopsis
Chapter 1 starts by introducing some representative algorithmic problems. We
begin immediately with the Stable Matching Problem, since we feel it sets
up the basic issues in algorithm design more concretely and more elegantly
than any abstract discussion could: stable matching is motivated by a natural
though complex real-world issue, from which one can abstract an interesting
problem statement and a surprisingly effective algorithm to solve this problem.
The remainder of Chapter 1 discusses a list of five “representative problems”
that foreshadow topics from the remainder of the course. These five problems
are interrelated in the sense that they are all variations and/or special cases
of the Independent Set Problem; but one is solvable by a greedy algorithm,
one by dynamic programming, one by network flow, one (the Independent
Set Problem itself) is NP-complete, and one is PSPACE-complete. The fact that
closely related problems can vary greatly in complexity is an important theme
of the book, and these five problems serve as milestones that reappear as the
book progresses.
Chapters 2 and 3 cover the interface to the CS1/CS2 course sequence
mentioned earlier. Chapter 2 introduces the key mathematical definitions and
notations used for analyzing algorithms, as well as the motivating principles
behind them. It begins with an informal overview of what it means for a prob-
lem to be computationally tractable, together with the concept of polynomial
time as a formal notion of efficiency. It then discusses growth rates of func-
tions and asymptotic analysis more formally, and offers a guide to commonly
occurring functions in algorithm analysis, together with standard applications
in which they arise. Chapter 3 covers the basic definitions and algorithmic
primitives needed for working with graphs, which are central to so many of
the problems in the book. A number of basic graph algorithms are often im-
plemented by students late in the CS1/CS2 course sequence, but it is valuable
to present the material here in a broader algorithm design context. In par-
ticular, we discuss basic graph definitions, graph traversal techniques such
as breadth-first search and depth-first search, and directed graph concepts
including strong connectivity and topological ordering.
xviii
Preface
Chapters 2 and 3 also present many of the basic data structures that will
be used for implementing algorithms throughout the book; more advanced
data structures are presented in subsequent chapters. Our approach to data
structures is to introduce them as they are needed for the implementation of
the algorithms being developed in the book. Thus, although many of the data
structures covered here will be familiar to students from the CS1/CS2 sequence,
our focus is on these data structures in the broader context of algorithm design
and analysis.
Chapters 4 through 7 cover four major algorithm design techniques: greedy
algorithms, divide and conquer, dynamic programming, and network flow.
With greedy algorithms, the challenge is to recognize when they work and
when they don’t; our coverage of this topic is centered around a way of clas-
sifying the kinds of arguments used to prove greedy algorithms correct. This
chapter concludes with some of the main applications of greedy algorithms,
for shortest paths, undirected and directed spanning trees, clustering, and
compression. For divide and conquer, we begin with a discussion of strategies
for solving recurrence relations as bounds on running times; we then show
how familiarity with these recurrences can guide the design of algorithms that
improve over straightforward approaches to a number of basic problems, in-
cluding the comparison of rankings, the computation of closest pairs of points
in the plane, and the Fast Fourier Transform. Next we develop dynamic pro-
gramming by starting with the recursive intuition behind it, and subsequently
building up more and more expressive recurrence formulations through appli-
cations in which they naturally arise. This chapter concludes with extended
discussions of the dynamic programming approach to two fundamental prob-
lems: sequence alignment, with applications in computational biology; and
shortest paths in graphs, with connections to Internet routing protocols. Fi-
nally, we cover algorithms for network flow problems, devoting much of our
focus in this chapter to discussing a large array of different flow applications.
To the extent that network flow is covered in algorithms courses, students are
often left without an appreciation for the wide range of problems to which it
can be applied; we try to do justice to its versatility by presenting applications
to load balancing, scheduling, image segmentation, and a number of other
problems.
Chapters 8 and 9 cover computational intractability. We devote most of
our attention to NP-completeness, organizing the basic NP-complete problems
thematically to help students recognize candidates for reductions when they
encounter new problems. We build up to some fairly complex proofs of NP-
completeness, with guidance on how one goes about constructing a difficult
reduction. We also consider types of computational hardness beyond NP-
completeness, particularly through the topic of PSPACE-completeness. We
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