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algorithm design and Analysis
This textbook is the core course of computer science and Technology "algorithm design and Analysis" textbook. "Algorithm design and Analysis" based on the algorithm design technology and analysis method as the main line to organize the knowledge units, the main content includes the basic knowledge, divide and conquer strategy, dynamic programming, greedy method, backtracking and branch clearance, algorithm analysis and problems of computational complexity, NP-complete, approximate algorithm, stochastic algorithm, dealing with the problem of difficult to solve the strategy. The book highlights the analysis of the problem itself and the interpretation of the method, from the problem modeling, algorithm design and analysis, improve measures and other aspects of the appropriate recommendations, but also briefly introduced the computational complexity theory of the core content and to deal with some of the difficult problems of new technologies.
"Algorithm design and analysis" There are supporting learning guidance and exercise analysis of the book and PPT electronic teaching plan.
"Algorithm design and analysis" can be used as university computer science and technology, software engineering, information security, information and computer science and other professional undergraduate and postgraduate teaching book, also can be engaged in practical problem solving algorithm design and analysis work of reference books.
The 1th Chapter basic Knowledge
1.1 Basic concepts of algorithms
Pseudo-code description of 1.2 algorithm
mathematical basis of the 1.3 algorithm
The asymptotic bounds of the 1.3.1 function
the method of 1.3.2 summation
1.3.3 Recursive equation solving method
Exercise 1
The 2nd chapter divided the treatment strategy
2.1 The basic idea of the strategy of divide and conquer
2.1.12 Familiar examples
A general description of the 2.1.2 divide-and-conquer algorithm
analysis Technology of 2.2-divide-and-conquer algorithm
2.3 Ways to improve the algorithm of divide and conquer
2.3.1 Reducing the number of sub-problems by algebraic transformation
2.3.2 using preprocessing to reduce the computational amount of recursive internal
2.4 Typical examples
2.4.1 Fast Sorting algorithm
2.4.2 Selection Questions
evaluation of 2.4.3 n-1 polynomial on all 2 n square root
Exercise 2
The 3rd Chapter dynamic Planning
3.1 Design idea of dynamic programming
3.1. The shortest path problem with more than 1 starting point and multiple end point
3.1.2 Requirements for using dynamic programming techniques
3.2 Design elements of dynamic programming algorithm
the division and recurrence equation of 3.2.1 Sub-problem
recursive implementation of 3.2.2 Dynamic programming algorithm
iterative implementation of 3.2.3 Dynamic programming algorithm
3.2.4 A simple instance of the computational process
3.3 Typical applications of dynamic programming algorithms
3.3.1 Investment Issues
3.3.2 knapsack problem
3.3.3 Longest common sub-sequence LCS
3.3.4 Image Compression
3.3.5 Maximum sub-segment and longest common child
3.3.6 Optimal Two-point search tree
the dynamic programming algorithm in 3.3.7 bioinformatics
Exercise 3
The 4th Chapter greedy Law
4.1 Design idea of greedy method
4.2 Proof of the correctness of the greedy method
4.3 How to deal with the optimal solution of greedy method
4.4 Typical application of greedy method
4.4.1 Optimal Prefix code
4.4.2 minimum Spanning tree
4.4.3 Single Source Shortest path
Exercise 4
the 5th Chapter backtracking and the branch gauge
the 6th Chapter algorithm analysis and the computational complexity of the problem
Chapter 7th NP completeness
The 8th chapter approximate algorithm
The 9th Chapter stochastic algorithm
the 10th chapter deals with the strategies of solving difficult problems
Reference Documents
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Algorithmic design and Analysis (Kuvanling) PDF
