Class warshal2

Mar 30, 2015Download as PPTX, PDF1 like957 views

This document discusses Warshall's algorithm for finding the transitive closure of a graph using dynamic programming. It begins with an introduction to transitive closure and how it relates to Facebook vs Google+ graph types. It then provides an explanation of Warshall's algorithm, including a visitation example and weighted example. It analyzes the time and space complexity of Warshall's algorithm as O(n3) and O(n2) respectively. It concludes with applications of Warshall's algorithm such as shortest paths and bipartite graph checking.

Warshall’s Algorithm
&
Dynamic Programming
BY
DEBARATI DAS ( CS-52)

Key Points OF Discussion
1
• FB vs Google+ (graph types)
• Transitive Closure
2
• Explanation of Warshall Algorithm
• Complexity Analysis
3
• Applications of Warshall Algorithm

Transitive Closure
1
• What is Transitivity ?
• Express it in a graph :
2
• What is Transitive Closure ?
• Express as a Matrix :
• How can we find Transitive Closure of A
Graph ?

Finding Transitive Closure
• Involves searching for the shortest path
starting at every vertex.
• If you start at ith vertex, after traversal you
get columns containing 1 in the ith row.
• Clearly it has a disadvantage (?)
DFS
Implementation
• It involves finding the shortest path and
improving the estimate on the path every
single time, till its optimal.
• However, this fails in case of negative
cycles.
Basic Point of
The Warshall
Algorithm

Complexity Analysis :
In this Bottom up Dynamic approach,
D0 -> 0
D1 -> n2 problems , every problem is a value in a cell.
n2xn where n2 denotes subproblems and n is no of matrices
So,total number of problems that need to be solved are n3
Therefore Time complexity is O(n3)
Space Complexity :
D1->D0
D2->D1
D3->D2
At any point of time you need two matrices so O(n2)

Applications of Warshall Algorithm
1. Shortest Path in Directed Graphs
2. Optimal Routing
3. Check if a graph is Bipartite
4. Pathfinder Networks
5. Inversion of Real Networks
(Gauss Jordan Method)

Key Questions
1
• What is the difference between D&C and DP ?
• What is Dynamic Programming ?
• Why is it called “Dynamic” ?
2
• What is Space Time Trade off ?
• How is it implemented by DP ?
• What are the applications of DP ?
• What is a non optimizable problem ?
3
• What is transitive Closure ?
• Warshall Algorithm discussion

Fibonacci Series : An illustration
fib(5)
fib(4) fib(3)
fib(3) fib(2) fib(2) fib(1)
fib(2) fib(1) fib(1) fib(0) fib(1) fib(0)
fib(1) fib(0)
6
5
21
3
4

Divide and Conquer vs D.P
 Clearly If We use Divide and Conquer, We face
OVER LAP.
 Mostly values are calculated again and again,
so even though access of function is O(1) , the
total time complexity is exponential or O(2n).
 INSTEAD if we used DP. We would store each
value in a table and access when required.
 DP Time complexity becomes O(n)

Why is it called “Dynamic
Programming” ?

Dynamic Programming : Basic Idea
 Avoid calculating the same thing twice by keeping a table of
known results, which we fill up as subinstances are solved.
 Dynamic programming is a bottom-up / top down technique
 Bottom up dynamic programming evaluates by computing all
function values in order, starting at lowest and using previously
computed values.
 Can MERGE SORT Be solved using DP ?

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This document discusses semantic analysis of C programs. It describes the stages of compiler design and focuses on the semantic analysis phase. The goals of semantic analysis are to check if the code forms sensible instructions, functions always return values, there are no unreachable statements, return types match function headers, and expressions are compatible with datatypes. The design of the system uses LEX, YACC, and Python to perform semantic analysis on an input C program and detect any semantic errors. Examples of semantic errors related to return values, unreachable code, and expression compatibility with datatypes are provided. Semantic analysis is important as it makes code more meaningful, flexible, and error-free while enabling more potential for optimization.

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This document summarizes key concepts in Boolean algebra including: 1) Defining a Boolean algebra using a set of elements and binary operations that follow specific axioms. 2) The two-valued Boolean algebra uses the elements 0 and 1 with AND, OR, and NOT operations. 3) Boolean functions consist of variables, constants, and logic operations and can be represented using truth tables. 4) Logic circuits can be designed to implement Boolean functions using gates like AND, OR, NOT, NAND, and NOR.

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This document summarizes key concepts in digital systems and binary numbers. It discusses why digital systems are preferred over analog, how to convert between number bases, signed and complement number representations, overflow, binary and decimal codes, BCD addition, Gray code, and parity checks. Digital systems are more cost effective, reliable, programmable and selective compared to analog. Number conversions involve grouping bits or dividing decimals. Signed number systems use complement representations to indicate positive and negative values.

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1. Warshall’s Algorithm & Dynamic Programming BY DEBARATI DAS ( CS-52)

2. Key Points OF Discussion 1 • FB vs Google+ (graph types) • Transitive Closure 2 • Explanation of Warshall Algorithm • Complexity Analysis 3 • Applications of Warshall Algorithm

3. FB Vs Google+

4. Transitive Closure 1 • What is Transitivity ? • Express it in a graph : 2 • What is Transitive Closure ? • Express as a Matrix : • How can we find Transitive Closure of A Graph ?

5. Finding Transitive Closure • Involves searching for the shortest path starting at every vertex. • If you start at ith vertex, after traversal you get columns containing 1 in the ith row. • Clearly it has a disadvantage (?) DFS Implementation • It involves finding the shortest path and improving the estimate on the path every single time, till its optimal. • However, this fails in case of negative cycles. Basic Point of The Warshall Algorithm

6. Warshall Algorithm : Visitation Example

7. Warshall Algorithm : Weighted Example

8. Warshall Algorithm, brief

9. Complexity Analysis : In this Bottom up Dynamic approach, D0 -> 0 D1 -> n2 problems , every problem is a value in a cell. n2xn where n2 denotes subproblems and n is no of matrices So,total number of problems that need to be solved are n3 Therefore Time complexity is O(n3) Space Complexity : D1->D0 D2->D1 D3->D2 At any point of time you need two matrices so O(n2)

10. Applications of Warshall Algorithm 1. Shortest Path in Directed Graphs 2. Optimal Routing 3. Check if a graph is Bipartite 4. Pathfinder Networks 5. Inversion of Real Networks (Gauss Jordan Method)

11. Key Questions 1 • What is the difference between D&C and DP ? • What is Dynamic Programming ? • Why is it called “Dynamic” ? 2 • What is Space Time Trade off ? • How is it implemented by DP ? • What are the applications of DP ? • What is a non optimizable problem ? 3 • What is transitive Closure ? • Warshall Algorithm discussion

12. Fibonacci Series : An illustration fib(5) fib(4) fib(3) fib(3) fib(2) fib(2) fib(1) fib(2) fib(1) fib(1) fib(0) fib(1) fib(0) fib(1) fib(0) 6 5 21 3 4

13. Divide and Conquer vs D.P  Clearly If We use Divide and Conquer, We face OVER LAP.  Mostly values are calculated again and again, so even though access of function is O(1) , the total time complexity is exponential or O(2n).  INSTEAD if we used DP. We would store each value in a table and access when required.  DP Time complexity becomes O(n)

14. Why is it called “Dynamic Programming” ?

15. Dynamic Programming : Basic Idea  Avoid calculating the same thing twice by keeping a table of known results, which we fill up as subinstances are solved.  Dynamic programming is a bottom-up / top down technique  Bottom up dynamic programming evaluates by computing all function values in order, starting at lowest and using previously computed values.  Can MERGE SORT Be solved using DP ?

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