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MST-Kruskal( G, w ). 1. A   // initially A is empty 2. for each vertex v  V [ G ] // 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

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Mst kruskal g w
MST-Kruskal(G, w)

1. A   // initially A is empty

2. for each vertex v  V[G] // line 2-3 takesO(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 bynondecreasing weight

6. do if Find-Set(u)  Find-Set(v) // u&v on different trees

7. then A  A  {(u,v)}

8. Union(u,v)

9. return A

Total running time is O(E lg E).


Analysis of kruskal
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)


Correctness of kruskal
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.


Intuition behind prim s algorithm
Intuition behind Prim’s 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!


Basics of prim s algorithm
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?


Implementation priority queue
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 u’s 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}}


Example prim s algorithm
Example: Prim’s Algorithm


Mst prim g w r
MST-Prim(G, w, r)

1. Q  V[G]

2. for each vertex u  Q // initialization: O(V) time

3. do key[u]  

4. key[r]  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  adj[u]

9. do if v  Qand w(u,v) < key[v]

10. then [v]  u

11. key[v]  w(u,v) // new lighter edge out of v

12. decrease_Key(Q, v, key[v])


Analysis of prim
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) wheren = |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 Kruskal’s: O((n+e) lg n) time


Correctness of prim
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.


Optimization problems
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)


Dynamic programming
Dynamic Programming

  • Similar to divide-and-conquer, it breaks problems down into smaller problems that are solved recursively.

  • In contrast to D&C, 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.


Elements of dp algorithms
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.


Applicability to optimization problems
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.