| 相对于理论来说，本书更注重实际运用，书中提供了多种程序语言中可用的有效代码解决方案，可轻而易举地适合一个特定的项目。 |
| George T.Heineman,Gary Pollice和Stanley Selkow 均为Worcester Polytechnic Institute（伍斯特理工学院）计算机科学系的教授。George是《Component－Based Software Engineering：Putting the Pieces Together》(Addison-Wesley）的合编者,Gary则是《Head First Object-Oriented Analysis and Design》的合著者。... .. << 查看详细 |
| preface . part i. 1. algorithms matter understand the problem experiment if necessary algorithms to the rescue side story the moral of the story references 2. the mathematics of algorithms size of a problem instance rate of growth of functions analysis in the best, average, and worst cases performance families mix of operations benchmark operations one final point references 3. patterns and domains patterns: a communication language .algorithm pattern format pseudocode pattern format design format empirical evaluation format domains and algorithms floating-point computations manual memory allocation choosing a programming language references part ii. 4. sorting algorithms overview insertion sort median sort quicksort selection sort heap sort counting sort bucket sort criteria for choosing a sorting algorithm references 5. searching overview sequential search binary search hash-based search binary tree search .. 6. graph algorithms overview depth-first search breadth-first search single-source shortest path all pairs shortest path minimum spanning tree algorithms references 7. path finding in ai overview depth-first search breadth-first search a*search comparison minimax negmax alphabeta references 8. network flow algorithms overview maximum flow bipartite matching reflections on augmenting paths minimum cost flow transshipment transportation assignment linear programming references 9. computational geometry overview convex hull scan linesweep nearest neighbor queries range queries references part iii. 10. when all else fails variations on a theme approximation algorithms offline algorithms parallel algorithms randomized algorithms algorithms that can be wrong, but with diminishing probability references 11. epilogue overview principle: know your data principle: decompose the problem into smaller problems principle: choose the right data structure principle: add storage to increase performance principle: if no solution is evident, construct a search principle: if no solution is evident, reduce your problem to another problem that has a solution principle: writing algorithms is hard—testing algorithms is harder part iv. appendix: benchmarking index ... |
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