Today's Lecture

Fitting last class, draws on much of what we've learned:

• Analysis: Critical for understanding why the problem is so difficult in the first place!
• Sorting: Used in Kruskal's algorithm.
• Statistics: Useful for comparing algorithms empirically.
• Heap: Used in A search.
• Binary Search Trees: splay trees provide efficient implementation of local-search heuristics.
• Matching: Important approximation method.
• Matrix Algorithms: LP relaxations used extensively in generating lower bounds.
• Graphs: Special type of graph problem, tree walk is a DFS.
• Minimum Spanning Tree: Simple approximation method.
• Shortest Paths: A search for optimal tour.
• DP: New approximation schemes for 2-d TSP.
• Complexity: NP complete.