The subset sum problem can be reliably solved optimally using the dynamic programming algorithm shown below: SubsetSum(n, W) Let B(0, w) = 0 for each w E {0, ..., W} for i ← 1 to n for w← 0 to W if w< w, then B(i, w) else B(i, w) B(i 1,w) max (w; + B(i − 1, ww₁), B(i - 1,w)) - where n is the number of requests, W is the maximum weight constraint, w, i the weight associated with request i, and B is the solution space. You are given a set of requests and their corresponding weights. The maximum weight constraint W is 12.

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The subset sum problem can be reliably solved optimally using the dynamic
programming algorithm shown below:
SubsetSum(n, W)
Let B(0, w) = 0 for each w E {0, ..., W}
for i ← 1 to n
for w← 0 to W
if w< w; then
←
B(i,w) B(i-1, w)
else
B(i, w) max (w; + B (i − 1,w – w₁), B(i - 1,w))
-
where n is the number of requests, W is the maximum weight constraint, w; is
the weight associated with request i, and B is the solution space.
You are given a set of requests and their corresponding weights. The
maximum weight constraint W is 12.
Transcribed Image Text:The subset sum problem can be reliably solved optimally using the dynamic programming algorithm shown below: SubsetSum(n, W) Let B(0, w) = 0 for each w E {0, ..., W} for i ← 1 to n for w← 0 to W if w< w; then ← B(i,w) B(i-1, w) else B(i, w) max (w; + B (i − 1,w – w₁), B(i - 1,w)) - where n is the number of requests, W is the maximum weight constraint, w; is the weight associated with request i, and B is the solution space. You are given a set of requests and their corresponding weights. The maximum weight constraint W is 12.
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Complete the solution space table to determine the optimal subset sum.
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Transcribed Image Text:i 6 5 4 3 2 1 0 O Complete the solution space table to determine the optimal subset sum. 0 1 2 i 1 2 3 4 5 6 3 4 5 W Wi 2 1 6 7 1 10 6 7 8 9 10 11 12
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