We are given:
# A weighted graph G where all edge weights are greater than one.
# A transformed graph G′ where each edge weight in G is squared.
# T is the Minimum Spanning Tree (MST) of G with total weight t.
# T′ is the MST of G′ with total weight t′.
Step 1: How Squaring Affects Edge Weights
# Suppose G has edge weights w1 ,w2 ,…,wm.
# In G′, each weight is squared: w1², w2², w3², … , wm²
# Since all edge weights are greater than one, squaring increases their values
(i.e., w² > w for w>1).
Step 2: Does the Same MST Structure Hold?
# In Kruskal’s algorithm, edges are picked in increasing order of weight.
# When we square weights, the relative order between small and large edges might change.
Example: If w1 < w2 , squaring might increase their gap (e.g., 2²=4 and 3² =9).
This means the set of edges selected in T might change in T′.
Conclusion:
T and T′ might be different because squaring can alter the priority of edges.
Thus, T′ ≠ T in general.
Step 3: What Happens to Total Weight?
Incorrect Claim: t′= t²
Why? The MST weight is the sum of selected edge weights, but
w1² + w2² + ⋯ + wn² ≠ (w1 +w2 +⋯+wn) ²
Conclusion: t′≠ t², meaning options A and C are false.
Step 4: Is t′ < t² ?
Since all edge weights increase when squared,
The weight of the MST must increase, meaning t′>t.
However, t′ is not necessarily less than t².
This means option B is also incorrect.
Final Conclusion:
Since none of the options A, B, or C hold, the correct answer is option D: None of the above.