Shortest paths Single-source shortest paths nonnegative edge weights Dijkstra's algorithm: O(E+ vlg n General

Paths in graphs Consider a digraph g=(v, E)with edge-weight function w:E→>R. The weight of path p=v1→ →>…→> vi is defined to be (D)=∑(n,1)

Dynamic programming Design technique, like divide-and-conquer Example: Longest Common Subsequence (LCs) Given two sequences x[l.. m] and yll.n], find a longest subsequence common to them both a' not the

Fixed -universe successor problem Goal: maintain a dynamic subset s of size n of the universe 0=10, 1,.,u-1 of size u subject to these operations INSERT(X∈U\\S): Add x to s DELETE(X E S): Remove x from S

ynamic order statistics OS-SELECT(i, S): returns the i th smallest element in the dynamic set S. OS-RANK(, S): returns the rank ofx E S in the sorted order of s s elements IDEA: Use a red-black tree for the set S, but keep subtree sizes in the nodes

Binary-search-tree sort 7∈ b Create an empty Bst for i=i to n do trEe-INSert(,AiD) Perform an inorder tree walk of t Example:

Symbol-table problem Symbol table T holding n records

How fast can we sort? All the sorting algorithms we have seen so far are comparison sorts: only use comparisons to determine the relative order of elements E. g, insertion sort, merge sort, quicksort heapsort The best worst-case running time that weve seen for comparison sorting is O(nIgn) Is o(nlgn) the best we can do?

The divide-and-conquer design paradigm 1. Divide the problem(instance) into subproblems 2. Conquer the subproblems by solving them recursively 3. Combine subproblem solutions

Welcome to Introduction to Algorithms, Fall 2001 Handouts 1. Course Information 2. Calendar 3. Registration (MIT students only) 4. References 5. Objectives and Outcomes 6. Diagnostic Survey