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Algorithms in a Nutshell A Practical Guide 2nd Edition by George Heineman, Gary Pollice, Stanley Selkow ISBN 1491912995 9781491912997

Name: Algorithms in a Nutshell A Practical Guide 2nd Edition by George Heineman, Gary Pollice, Stanley Selkow ISBN 1491912995 9781491912997
SKU: EB-5389404
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Algorithms in a Nutshell A Practical Guide 2nd Edition George T. Heineman Digital Instant Download

Author(s): George T. Heineman, Gary Pollice, Stanley Selkow

ISBN(s): 9781491948927, 1491948922

Edition: 2

File Details: PDF, 15.19 MB

Year: 2016

Language: English

SKU: EB-5389404 Category: Computers - Algorithms and Data Structures Tags: Gary Pollice, George T. Heineman, Stanley Selkow

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Algorithms in a Nutshell A Practical Guide 2nd Edition by George T. Heineman, Gary Pollice, Stanley Selkow – Ebook PDF Instant Download/Delivery: 1491912995, 978-1491912997
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ISBN 10: 1491912995
ISBN 13: 978-1491912997
Author: George T. Heineman, Gary Pollice, Stanley Selkow

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 Python 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

Algorithms in a Nutshell A Practical Guide 2nd Table of contents:

Chapter 1. Thinking in Algorithms

Understand the Problem
Naïve Solution
Intelligent Approaches
- Greedy
- Divide and Conquer
- Parallel
- Approximation
- Generalization
Summary
References

Chapter 2. The Mathematics of Algorithms

Size of a Problem Instance
Rate of Growth of Functions
Analysis in the Best, Average, and Worst Cases
- Worst Case
- Average Case
- Best Case
Lower and Upper Bounds
Performance Families
- Constant Behavior
- Log n Behavior
- Sublinear O(nd) Behavior for d < 1
- Linear Performance
- Linearithmic Performance
- Quadratic Performance
- Less Obvious Performance Computations
- Exponential Performance
Summary of Asymptotic Growth
Benchmark Operations
References

Chapter 3. Algorithm Building Blocks

Algorithm Template Format
- Name
- Input/Output
- Context
- Solution
- Analysis
- Variations
Pseudocode Template Format
Empirical Evaluation Format
Floating-Point Computation
- Performance
- Rounding Error
- Comparing Floating-Point Values
- Special Quantities
Example Algorithm
- Name and Synopsis
- Input/Output
- Context
- Solution
- Analysis
Common Approaches
- Greedy
- Divide and Conquer
- Dynamic Programming
References

Chapter 4. Sorting Algorithms

Transposition Sorting
- Insertion Sort
  - Context
  - Solution
  - Analysis
- Selection Sort
- Heap Sort
  - Context
  - Solution
  - Analysis
Partition-Based Sorting
- Context
- Solution
- Analysis
Sorting without Comparisons
- Bucket Sort
  - Solution
  - Analysis
Sorting with Extra Storage
- Merge Sort
  - Input/Output
  - Solution
  - Analysis
String Benchmark Results
Analysis Techniques
References

Chapter 5. Searching

Sequential Search
- Input/Output
- Context
- Solution
- Analysis
Binary Search
- Input/Output
- Context
- Solution
- Analysis
Hash-Based Search
Bloom Filter
Binary Search Tree
References

Chapter 6. Graph Algorithms

Graphs and Data Structure Design
Depth-First Search
Breadth-First Search
Single-Source Shortest Path
- Dijkstra’s Algorithm for Dense Graphs
- Benchmark Data (Dense and Sparse Graphs)
All-Pairs Shortest Path
Minimum Spanning Tree Algorithms
Final Thoughts on Graphs
- Storage Issues
- Graph Analysis
References

Chapter 7. Path Finding in AI

Game Trees
- Static Evaluation Functions
Path-Finding Concepts
- Representing State
- Calculating Available Moves
- Maximum Expansion Depth
Minimax
NegMax
AlphaBeta
Search Trees
Path-Length Heuristic Functions
Depth-First Search and Breadth-First Search
A* Search
Comparing Search-Tree Algorithms
References

Chapter 8. Network Flow Algorithms

Network Flow and Maximum Flow
Optimization and Related Algorithms
- Bipartite Matching
- Minimum Cost Flow
- Transshipment
- Transportation
- Assignment
Linear Programming
References

Chapter 9. Computational Geometry

Classifying Problems
- Input Data, Computation, Nature of the Task, Assumptions
Convex Hull
Computing Line-Segment Intersections
- LineSweep
Voronoi Diagram
References

Chapter 10. Spatial Tree Structures

Nearest Neighbor Queries
Range Queries
Intersection Queries
Spatial Tree Structures
- k-d Tree
- Quadtree
- R-Tree
Range Query
References

Chapter 11. Emerging Algorithm Categories

Variations on a Theme
- Approximation Algorithms
- Parallel Algorithms
- Probabilistic Algorithms
Estimating the Size of a Set and Search Tree
References

Chapter 12. Epilogue: 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

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