كتاب Introduction to Algorithms, Second Edition

Introduction to Algorithms, Second Edition

كتاب Introduction to Algorithms, Second Edition 4646

Aimed at any serious
programmer or computer science student, the new second edition of
Introduction to Algorithms builds on the tradition of the original with a
truly magisterial guide to the world of algorithms. Clearly presented,
mathematically rigorous, and yet approachable even for the math-averse,
this title sets a high standard for a ****book and reference to the best
algorithms for solving a wide range of computing problems.With sample
problems and mathematical proofs demonstrating the correctness of each
algorithm, this book is ideal as a ****book for classroom study, but its
reach doesn't end there. The authors do a fine job of explaining each
algorithm. (Reference sections on basic mathematical notation will help
readers bridge the gap, but it will help to have some math background to
appreciate the full achievement of this handsome hardcover volume.)
Every algorithm is presented in pseudo-code, which can be implemented in
any computer ********, including C/C++ and Java. This ecumenical
approach is one of the book's strengths. When it comes to sorting and
common data structures, from basic linked lists to trees (including
binary trees, red-black, and B-trees), this title really shines, with
clear diagrams that show algorithms in operation. Even if you just
glance over the mathematical notation here, you can definitely benefit
from this **** in other ways.The book moves forward with more advanced
algorithms that implement strategies for solving more complicated
problems (including dynamic programming techniques, greedy algorithms,
and amortized analysis). Algorithms for graphing problems (used in such
real-world business problems as optimizing flight schedules or flow
through pipelines) come next. In each case, the authors provide the best
from current research in each topic, along with sample solutions.This
**** closes with a grab bag of useful algorithms including matrix
operations and linear programming, evaluating polynomials, and the
well-known Fast Fourier Transformation (FFT) (useful in signal
processing and engineering). Final sections on "NP-complete" problems,
like the well-known traveling salesman problem, show off that while not
all problems have a demonstrably final and best answer, algorithms that
generate acceptable approximate solutions can still be used to generate
useful, real-world answers.Throughout this ****, the authors anchor
their discussion of algorithms with current examples drawn from
molecular biology (like the Human Genome Project), business, and
engineering. Each section ends with short discussions of related
historical material, often discussing original research in each area of
algorithms. On the whole, they argue successfully that algorithms are a
"technology" just like hardware and software that can be used to write
better software that does more, with better performance. Along with
classic books on algorithms (like Donald Knuth's three-volume set, The
Art of Computer Programming), this title sets a new standard for
compiling the best research in algorithms. For any experienced
developer, regardless of their chosen ********, this **** deserves a
close look for extending the range and performance of real-world
software. --Richard Dragan Topics covered: Overview of algorithms
(including algorithms as a technology); designing and analyzing
algorithms; asymptotic notation; recurrences and recursion;
probabilistic analysis and randomized algorithms; heapsort algorithms;
priority queues; quicksort algorithms; linear time sorting (including
radix and bucket sort); medians and order statistics (including minimum
and maximum); introduction to data structures (stacks, queues, linked
lists, and rooted trees); hash tables (including hash functions); binary
search trees; red-black trees; augmenting data structures for custom
applications; dynamic programming explained (including assembly-line
scheduling, matrix-chain multiplication, and optimal binary search
trees); greedy algorithms (including Huffman codes and task-scheduling
problems); amortized analysis (the accounting and potential methods);
advanced data structures (including B-trees, binomial and Fibonacci
heaps, representing disjoint sets in data structures); graph algorithms
(representing graphs, minimum spanning trees, single-source shortest
paths, all-pairs shortest paths, and maximum flow algorithms); sorting
networks; matrix operations; linear programming (standard and slack
forms); polynomials and the Fast Fourier Transformation (FFT); number
theoretic algorithms (including greatest common divisor, modular
arithmetic, the Chinese remainder theorem, RSA public-key encryption,
primality testing, integer factorization); string matching;
computational geometry (including finding the convex hull);
NP-completeness (including sample real-world NP-complete problems and
their insolvability); approximation algorithms for NP-complete problems
(including the traveling salesman problem); reference sections for
summations and other mathematical notation, sets, relations, functions,
graphs and trees, as well as counting and probability backgrounder (plus
geometric and binomial distributions).

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