single source shortest path practice question

Duration: 4 min

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  1. About Course: About the Course
  2. CS | 01 | Digital Logic and Circuits and Discrete Mathematical Structures: Digital Systems & Boolean Basics, Number System, Number Representation, Logic Gates & Hardware, Boolean Expression, Boolean Minimization, Combinational Circuit, Sequential Circuits, Set Theory, Relations, Functions, Graph Theory, Group Theory, Propositional and Predicate Logic, Permutation and Combination
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  4. CS | 03 | Data Structures and Algorithm: Introduction to DS, Array, Stack, Queue, Linked List, Tree, Graphs, Hashing, Algorithm Analysis, Time Complexity Analysis, Sorting Algorithms, Greedy Algorithms, Dynamic Programming, Minimum Spanning Trees, Shortest Path Algos, Complexity Theory, Advanced Algorithms
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  14. CS | 13 | Computer Graphics: Basics of Computer Graphics, 2-D Geometrical Transforms and Viewing, 3-D Object Representation, Geometric Transformations and Viewing
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  19. GS Paper | 05 | Elementary Maths: Number System, Divisibility Rules, HCF LCM, Percentage, Profit and Loss, Simple Interest and Compound Interest, Ratio and Proportion (Ratios), Average, Time and Work, Speed Time and Distance, Algebra, Mensuration and Geometry, Data Interpretation, Arithmetic, Statistics, Probability, Sequence and Series (Series), Mixture and Alligation, Calculus, Trigonometry, Vectors
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AI summary & chapters

AI Summary

An AI-generated summary of this video lecture.

The video addresses a theoretical question in graph theory concerning the stability of shortest paths under uniform weight modification. The instructor begins by presenting the problem statement displayed on the screen: "If we increase weight of every edge by 1, the shortest path always remains same." He asks if this statement is true or false. To investigate this, he constructs a counterexample on the whiteboard. He writes "ex." and draws a graph with a source node 'S' on the left and a destination node 't' on the right. He creates two distinct paths between them. The first path, S -> A -> B -> C -> D -> t, consists of 5 edges with weights 2, 4, 5, 6, and 7 respectively, drawn in the upper section. The second path, S -> E -> t, consists of 2 edges with weights 10 and 15, drawn in the lower section.

Initially, the total weight of the first path is calculated as 2 + 4 + 5 + 6 + 7 = 24. The total weight of the second path is 10 + 15 = 25. Since 24 is less than 25, the first path is the shortest path. The instructor then applies the condition of the problem: increasing every edge weight by 1. The new weights for the first path become 3, 5, 6, 7, and 8, summing to 29. The new weights for the second path become 11 and 16, summing to 27. Comparing the new totals, 27 is now less than 29. This demonstrates that the shortest path has shifted from the first path to the second path. Consequently, the instructor concludes that the statement is false, marking "No" as the correct answer. He circles the "No" option to emphasize the conclusion.

Chapters

  1. 0:00 – 2:00 00:00-02:00

    The instructor introduces the problem statement regarding shortest paths and edge weight modification. He begins drawing a graph with source 'S' and destination 't', creating a multi-edge path S->A->B->C->D->t and a two-edge path S->E->t. He writes down the initial weights for each edge: 2, 4, 5, 6, 7 for the first path and 10, 15 for the second.

  2. 2:00 – 4:21 02:00-04:21

    The instructor calculates the initial total weights (24 vs 25) to establish the baseline shortest path. He then applies the transformation of adding 1 to every edge weight. He writes the new weights (3, 5, 6, 7, 8 and 11, 16) and calculates the new totals (29 vs 27). He demonstrates that the path with fewer edges becomes shorter, proving the statement false and selecting option (B) No.

The lesson demonstrates a critical property of shortest path algorithms: they are sensitive to uniform weight shifts if paths have different numbers of edges. By adding a constant to every edge, paths with more edges accumulate a larger total increase than paths with fewer edges. This example highlights that shortest paths are not invariant under such transformations, a key concept for understanding algorithm behavior in weighted graphs.

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