Librería Portfolio

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



Chapter 1Thinking in Algorithms
Understand the Problem
Naïve Solution
Intelligent Approaches
Summary
References
Chapter 2The Mathematics of Algorithms
Size of a Problem Instance
Rate of Growth of Functions
Analysis in the Best, Average, and Worst Cases
Performance Families
Benchmark Operations
References
Chapter 3Algorithm Building Blocks
Algorithm Template Format
Pseudocode Template Format
Empirical Evaluation Format
Floating-Point Computation
Example Algorithm
Common Approaches
References
Chapter 4Sorting Algorithms
Terminology
Representation
Comparable Elements
Stable Sorting
Criteria for Choosing a Sorting Algorithm
Transposition Sorting
Selection Sort
Heap Sort
Partition-Based Sorting
Sorting without Comparisons
Bucket Sort
Sorting with Extra Storage
String Benchmark Results
Analysis Techniques
References
Chapter 5Searching
Sequential Search
Binary Search
Hash-Based Search
Bloom Filter
Binary Search Tree
References
Chapter 6Graph Algorithms
Graphs
Depth-First Search
Breadth-First Search
Single-Source Shortest Path
Dijkstra's Algorithm for Dense Graphs
Comparing Single-Source Shortest-Path Options
All-Pairs Shortest Path
Minimum Spanning Tree Algorithms
Final Thoughts on Graphs
References
Chapter 7Path Finding in AI
Game Trees
Path-Finding Concepts
Minimax
NegMax
AlphaBeta
Search Trees
Depth-First Search
Breadth-First Search
A*Search
Comparing Search-Tree Algorithms
References
Chapter 8Network Flow Algorithms
Network Flow
Maximum Flow
Bipartite Matching
Reflections on Augmenting Paths
Minimum Cost Flow
Transshipment
Transportation
Assignment
Linear Programming
References
Chapter 9Computational Geometry
Classifying Problems
Convex Hull
Convex Hull Scan
Computing Line-Segment Intersections
LineSweep
Voronoi Diagram
References
Chapter 10Spatial Tree Structures
Nearest Neighbor Queries
Range Queries
Intersection Queries
Spatial Tree Structures
Nearest Neighbor Queries
Range Query
Quadtrees
R-Trees
References
Chapter 11Emerging Algorithm Categories
Variations on a Theme
Approximation Algorithms
Parallel Algorithms
Probabilistic Algorithms
References
Chapter 12Epilogue: Principles of Algorithms
Know Your Data
Decompose a Problem into Smaller Problems
Choose the Right Data Structure
Make the Space versus Time Trade-Off
Construct a Search
Reduce Your Problem to Another Problem
Writing Algorithms Is Hard-Testing Algorithms Is Harder
Accept Approximate Solutions When Possible
Add Parallelism to Increase Performance
Appendix Benchmarking
Statistical Foundation
Example
Reporting
Precision