MST-Kruskal(G,%20w)

1
MST-Kruskal(G, w)

• 1. A ? ? // initially A is empty
• 2. for each vertex v ? VG // line 2-3
  takes O(V) time
• 3. do Create-Set(v) // create set for
  each vertex
• 4. sort the edges of E by nondecreasing weight w
  
• 5. for each edge (u,v) ? E, in order by
  nondecreasing weight
• 6. do if Find-Set(u) ? Find-Set(v) // uv
  on different trees
• 7. then A ? A ? (u,v)
• 8. Union(u,v)
• 9. return A
• Total running time is O(E lg E).

2
Analysis of Kruskal

• Lines 1-3 (initialization) O(V)
• Line 4 (sorting) O(E lg E)
• Lines 6-8 (set operations) O(E log E)
• Total O(E log E)

3
Correctness of Kruskal

• Idea Show that every edge added is a safe edge
  for A
• Assume (u, v) is next edge to be added to A.
• Will not create a cycle
• Let A denote the tree of the forest A that
  contains vertex u. Consider the cut (A, V-A).
• This cut respects A (why?)
• and (u, v) is the light edge across the cut
  (why?)
• Thus, by the MST Lemma, (u,v) is safe.

4
Intuition behind Prims Algorithm

• Consider the set of vertices S currently part of
  the tree, and its complement (V-S). We have a
  cut of the graph and the current set of tree
  edges A is respected by this cut.
• Which edge should we add next? Light edge!

5
Basics of Prim s Algorithm

• It works by adding leaves on at a time to the
  current tree.
• Start with the root vertex r (it can be any
  vertex). At any time, the subset of edges A forms
  a single tree. S vertices of A.
• At each step, a light edge connecting a vertex in
  S to a vertex in V- S is added to the tree.
• The tree grows until it spans all the vertices in
  V.
• Implementation Issues
• How to update the cut efficiently?
• How to determine the light edge quickly?

6
Implementation Priority Queue

• Priority queue implemented using heap can support
  the following operations in O(lg n) time
• Insert (Q, u, key) Insert u with the key value
  key in Q
• u Extract_Min(Q) Extract the item with
  minimum key value in Q
• Decrease_Key(Q, u, new_key) Decrease the value
  of us key value to new_key
• All the vertices that are not in the S (the
  vertices of the edges in A) reside in a priority
  queue Q based on a key field. When the algorithm
  terminates, Q is empty. A (v, ?v) v ? V -
  r

7
Example Prims Algorithm

8
MST-Prim(G, w, r)

• 1. Q ? VG
• 2. for each vertex u ? Q // initialization
  O(V) time
• 3. do keyu ? ?
• 4. keyr ? 0 // start at the root
• 5. ?r ? NIL // set parent of r to be NIL
• 6. while Q ? ? // until all vertices in MST
• 7. do u ? Extract-Min(Q) // vertex with
  lightest edge
• 8. for each v ? adju
• 9. do if v ? Q and w(u,v) lt
  keyv
• 10. then ?v ? u
• 11. keyv ?
  w(u,v) // new lighter edge out of v
• 12.
  decrease_Key(Q, v, keyv)

9
Analysis of Prim

• Extracting the vertex from the queue O(lg n)
• For each incident edge, decreasing the key of the
  neighboring vertex O(lg n) where n V
• The other steps are constant time.
• The overall running time is, where e E
• T(n) ?u?V(lg n deg(u) lg n) ?u?V (1
  deg(u)) lg n
• lg n (n 2e) O((n e) lg n)
• Essentially same as Kruskals O((ne) lg n) time

10
Correctness of Prim

• Again, show that every edge added is a safe edge
  for A
• Assume (u, v) is next edge to be added to A.
• Consider the cut (A, V-A).
• This cut respects A (why?)
• and (u, v) is the light edge across the cut
  (why?)
• Thus, by the MST Lemma, (u,v) is safe.

11
Optimization Problems

• In which a set of choices must be made in order
  to arrive at an optimal (min/max) solution,
  subject to some constraints. (There may be
  several solutions to achieve an optimal value.)
• Two common techniques
• Dynamic Programming (global)
• Greedy Algorithms (local)

12
Dynamic Programming

• Similar to divide-and-conquer, it breaks problems
  down into smaller problems that are solved
  recursively.
• In contrast to DC, DP is applicable when the
  sub-problems are not independent, i.e. when
  sub-problems share sub-subproblems. It solves
  every sub-subproblem just once and saves the
  results in a table to avoid duplicated
  computation.

13
Elements of DP Algorithms

• Substructure decompose problem into smaller
  sub-problems. Express the solution of the
  original problem in terms of solutions for
  smaller problems.
• Table-structure Store the answers to the
  sub-problem in a table, because sub-problem
  solutions may be used many times.
• Bottom-up computation combine solutions on
  smaller sub-problems to solve larger
  sub-problems, and eventually arrive at a solution
  to the complete problem.

14
Applicability to Optimization Problems

• Optimal sub-structure (principle of optimality)
  for the global problem to be solved optimally,
  each sub-problem should be solved optimally.
  This is often violated due to sub-problem
  overlaps. Often by being less optimal on one
  problem, we may make a big savings on another
  sub-problem.
• Small number of sub-problems Many NP-hard
  problems can be formulated as DP problems, but
  these formulations are not efficient, because the
  number of sub-problems is exponentially large.
  Ideally, the number of sub-problems should be at
  most a polynomial number.