Analyze its runtime complexity and express it as a function U(N) of the total number N:= m+n of input characters. You can assume for simplicity that m²n=N/2.

Computer Networking: A Top-Down Approach (7th Edition)
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Chapter1: Computer Networks And The Internet
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Now consider the so-called Wagner-Fischer algorithm for computing the
Levenshtein distance:
WF (a[1...m), b[1...n])
Let D(0...m][0...n] be matrix of zeros
For i in (1...m] do
D[i] [0] = i
End For
For j in [1...n] do
D[0] [j] - j
End For
For i in (1...m] do
For j in [1...n] do
4
If ali] = b[j] Then
D[i] [j]
D[i-1] [j-1]
Else
D[i] [j] = min(D[i-1] [j],
D[i][j-1],
D[i-1] [j-1]) + 1
End If
End For
End For
Return D[m] [n]
End WF.
13.
Analyze its runtime complexity and express it as a function U(N)
of the total number N:= m+n of input characters. You can assume for
simplicity that m=nzN/2.
Transcribed Image Text:Now consider the so-called Wagner-Fischer algorithm for computing the Levenshtein distance: WF (a[1...m), b[1...n]) Let D(0...m][0...n] be matrix of zeros For i in (1...m] do D[i] [0] = i End For For j in [1...n] do D[0] [j] - j End For For i in (1...m] do For j in [1...n] do 4 If ali] = b[j] Then D[i] [j] D[i-1] [j-1] Else D[i] [j] = min(D[i-1] [j], D[i][j-1], D[i-1] [j-1]) + 1 End If End For End For Return D[m] [n] End WF. 13. Analyze its runtime complexity and express it as a function U(N) of the total number N:= m+n of input characters. You can assume for simplicity that m=nzN/2.
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