preface to the second edition 1. thinking in algorithms understand the problem naive solution intelligent approaches summary 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 benchmark operations references 3. algorithm buil blocks algorithm temte format eudocode temte format empirical evaluation format floating-point putation example algorithm mon approaches references 4. sorting algorithms transition sorting selection sort heap sort partition-based sorting sorting without parisons bucket sort sorting with extra storage string benchmark results analysis techniques references 5. searching sequential search binary search hash-based search bloom filter binary search tree references 6. graph algorithms graphs depth-first search breadth-first search single-source shortest path dijkstras algorithm for dense graphs paring single-source shortest-path options all-pairs shortest path minimum spanning tree algorithms final thoughts on graphs references 7. path fin in ai game trees path-fin concepts minimax negmax alphabeta search trees depth-first search breadth-first search asearch paring search-tree algorithms references 8. work flow algorithms work flow mamum flow bipartite matching reflections on augmenting paths minimum cost flow transshipment transportation assignment linear programming references 9. putational ge0metry classifying problems convex hull convex hull scan puting line-segment intersections linesweep voronoi diagram references 10. spatial tree structures nearest neior queries range queries intersection queries spatial tree structures nearest neior queries range query quadtrees r-trees references 11. emerging algorithm categories variations on a theme appromation algorithms parallel algorithms probabilistic algorithms references 12. epilogue: principles of algorithms know your data dee 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 appromate solutions when sible add parallelism to increase performance a. benchmarking index
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