Download Algorithms (Алгоритмы) by Robert Sedgewick PDF

By Robert Sedgewick

Из предисловия к книге
"...The e-book contains 40 chapters that are grouped into seven significant components: mathematical algorithms, sorting, looking out, string processing, geometric algorithms, graph algorithms and complex themes. a huge objective within the improvement of this e-book has been to collect the elemental tools from those diversified components, to be able to offer entry to the easiest equipment that we all know for fixing difficulties through machine for as many of us as possible."

Некоторое время назад на сайте были опубликованы первый и второй тома "Фундаментальных алгоритмов на С++" Роберта Седжвика. Книга Algorithms - одна из ранних публикаций (1983 год) этого автора, на русский язык она не переводилась.

Книга рассчитана на тех, кто уже немного знаком с основами программирования (скорее студентов, нежели школьников), фрагменты программ приведены на языке Pascal, в конце каждой главы имеются упражнения.

Алгоритмы описываются весьма кратко и достаточно простым языком (простота касается и английского языка - чтение книги вряд ли будет более трудным, чем чтение справочной информации в современных системах программирования). Представляется удобным то, что большое количество популярных алгоритмов
собраны под одной обложкой. Это позволяет использовать книгу и в качестве справочника.

Конечно, работу Седжвика трудно сравнивать по фундаментальности и строгости с замечательной книгой "Алгоритмы. Построение и анализ" Кормена, Лейзерсона, Ривеста и Штайна, но знакомство с первой может оказаться полезным при изучении второй.

Скан не мой, был когда-то найден в сети. Как уже говорилось, качество его умеренно хорошее: в некоторых формулах (реже в программах) встречаются ошибки распознавания. Однако в большинстве случаев правильный символ может быть легко "восстановлен".

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Additional info for Algorithms (Алгоритмы)

Sample text

As a rule, random number generators are fragile and need to be treated with respect. It’s difficult to be sure that a particular generator is good without investing an enormous amount of effort in doing the various statistical tests that have been devised. The moral is: do your best to use a good generator, based on the mathematical analysis and the experience of others; just to be sure, examine the numbers to make sure that they “look” random; if anything goes wrong, blame the random number generator!

Once the program is written, the numbers that it will produce can be deduced, so how could they be random? The best we can hope to do is to write programs which produce isequences of numbers having many of the same properties as random numbers. Such numbers are commonly called pseudo-random numbers: they’re not really random, but they can be useful 33 CHAF’TER 3 as approximations to random numbers, in much the same way that floatingpoint numbers are useful as approximations to real numbers. (Sometimes it’s convenient to make a further distinction: in some situations, a few properties of random numbers are of crucial interest while others are irrelevant.

Interpolation The “inverse” problem to the problem of evaluating a polynomial of degree N at N points simultaneously is the problem of polynomial interpolation: given a set of N points x1 ,x2,. . ,xN and associated values yr,y2,. . ,yN, find the unique polynomial of degree N - 1 which1 has p(Xl)= Yl,P(zz)= Y21 . '(xN) = YN. The interpolation problem is to find the polynomial, given a set of points and values. The evaluation problem is to find the values, given the polynomial and the points. ) The classic solution to the interpolation problem is given by Lagrange’s interpolation formula, which is often used as a proof that a polynomial of degree N - 1 is completely determined by N points: This formula seems formidable at first but is actually quite simple.

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