Download Slides for Exam 3 review

Review for Final Exam
• Non-cumulative, covers material since exam 2
• Data structures covered:
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Treaps
Hashing
Skip lists
Disjoint sets
Graphs
• For each of these data structures
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Basic idea of data structure and operations
Be able to work out small example problems
Prove related theorems
Advantages and limitations
Asymptotic time performance
Comparison
• Review questions are available on the web.
Treaps
• Definition
– Two values associated with each node
• Key: making it a BST
• Priority: making is binary min heap
– Priorities are randomly generated
• Making treap a BST constructed from a randomly ordered sequence of
keys
• Main advantages
– High probability to be balanced (O(logn) height)
– Compare with splay tree and RB tree
• Operations
– Find: according to key values as if it is a BST
– Insert: as a leaf first as in BST, then rotate it to satisfy heap order
– Delete: rotate the node to be deleted down according to heap order until
it becomes a leaf, then delete it.
– Support set union, partition
Hashing
– Hash table
• Table size (primes)
• Trading space for time
– Hashing functions
• Properties making a good hashing function
• Examples of division and multiplication hashing functions
• Operations (insert/remove/find/)
– Collision management
• Separate chaining
• Open addressing (different probing techniques, clustering problem)
– Worst case time performance:
• O(1) for find/insert/delete if  is small and hashing function is good
– Limitations
• Hard to answer order based queries (successor, min/max, etc.)
Skip Lists
– What is a skip list
• Nodes with different size (different # of skip pointers)
• Node size distribution according to the associated probability p
– Nodes with different size do not have to follow a rigid
pattern
– The expected # of nodes with exactly i pointers (pi-1(1- p))
– How to determine the size of the head node (log1/p N)
– Why need skip lists
• Expected time performance O(lg N) for find/insert/remove
• Probabilistically determining node size facilitate insert/remove
operations
• Advantages over sorted arrays, sorted list, BST, balanced BST
– Skip list operations
• find
• insert (how to determine the size of the new node)
• Set pointers in insert and remove operations (backLook node)
– Performance
• Expected time performance O(lg N) for find/insert/remove (very
small prob. of poor performance when N is large)
• Expected # of pointers per node: 1/(1 - p)
Disjoint Sets
– Equivalence relation and equivalence class
• definitions and examples
– Disjoint sets and up-tree representation
• representative of each set
• direction of pointers
– Union-find operations
• basic union and find operation
• path compression (for find) and union by weight heuristics
• time performance when the two heuristics are used:
O(m lg* n) for m operations (what does lg* n mean)
O(1) amortized time for each operation
Graphs
– Graph definitions
• G = (V, E), directed and undirected graphs, DAG
• path, path length (with/without weights), cycle, simple path
• connectivity, connected component, connected graph,
complete graph, strongly and weakly connectedness.
– Adjacency and representation
• adjacency matrix and adjacency lists, when to use which
• time performance with each
– Graph traversal: DF and BF
– Single source shortest path
• Breadth first (with unweighted edges)
• Dijkstra’s algorithm (with weighted edges)
– Topological order (for DAG)
• What is a topological order (definitions of predecessor,
successor, strict partial order)
• Algorithm for topological sort