# Algorithms in a Nutshell: A Practical Guide

Language: English

Pages: 390

ISBN: 1491948922

Format: PDF / Kindle (mobi) / ePub

Creating robust software requires the use of efficient algorithms, but programmers seldom think about them until a problem occurs. This updated edition of *Algorithms in a Nutshell* describes a large number of existing algorithms for solving a variety of problems, and helps you select and implement the right algorithm for your needs—with just enough math to let you understand and analyze algorithm performance.

With its focus on application, rather than theory, this book provides efficient code solutions in several programming languages that you can easily adapt to a specific project. Each major algorithm is presented in the style of a design pattern that includes information to help you understand why and when the algorithm is appropriate.

With this book, you will:

- Solve a particular coding problem or improve on the performance of an existing solution
- Quickly locate algorithms that relate to the problems you want to solve, and determine why a particular algorithm is the right one to use
- Get algorithmic solutions in C, C++, Java, and Ruby with implementation tips
- Learn the expected performance of an algorithm, and the conditions it needs to perform at its best
- Discover the impact that similar design decisions have on different algorithms
- Learn advanced data structures to improve the efficiency of algorithms

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Representation Primitive type Sign Exponent Mantissa Float 1 bit 8 bits 23 bits Double 1 bit 11 bits 52 bits Sample Representation of 3.88 as (0x407851ec) 01000000 01111000 01010001 11101100 (total of 32 bits) s mmmmmmm mmmmmmmm mmmmmmmm eeeeeee e The next three consecutive floating-point representations (and values) are: 0x407851ec 3.88 0x407851ed 3.8800004 0x407851ee 3.8800006 0x407851ef 3.8800008 Here are the floating-point values for three randomly chosen 32-bit.

Within the references at the end of a chapter. Using Code Examples This book is here to help you get your job done. In general, you may use the code in this book in your programs and documentation. You do not need to contact us for permission unless you're reproducing a significant portion of the code. For example, writing a program that uses several chunks of code from this book does not require permission. Selling or distributing a CD-ROM of examples from O'Reilly books does require.

The black squares in the figure represents a pivot selection in line 1 of partition. The first pivot selected is "2", which turns out to be a poor choice since it produces two subarrays of size 1 and size 14. During the next recursive invocation of Quicksort on the right subarray, "12" is selected to be the pivot, which produces two subarrays of size 9 and 4, respectively. Already you can see the benefit of using partition since the last four elements in the array are, in fact, the largest four.

"store elements, no lists" option in Figure 5-4), then you need to deal with collisions; otherwise, elements inserted into the hash table may be lost. [16] Hash-based searches require the complete word before the search can begin. With a binary search approach, one may search for the work incrementally as it is being typed, but this introduces additional complexity to the program. [17] log (200,000) = 17.61 [18] The code can be downloaded from the Open JDK website at http://openjdk.java.net.

Can secure a win if O makes a poor move choice. The scores associated with each of the leaf game states are evaluated from that player's perspective (in this case, the original player O). Note how the score for the initial game state is −2, because that is the "maximum of the negative scores of its children." Figure 7-19. Sample NegMax exploration The number of states explored by NegMax is the same as Minimax, or on the order of bd for a d-ply search with a fixed number b of moves at each game.