Sliding Window Algorithm Explained | Built In
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Sliding Window Algorithm Explained | Built In
The sliding window algorithm is a technique that streamlines nested loops into a single loop to process contiguous elements in arrays or strings, reducing time complexity and improving efficiency for problems like max sum, averages or pattern matching.
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Sliding Window Algorithm
Suppose that we run the sliding window algorithm with SWS=5 and RWS =3, and no out-of-order arrivals. (a) Find the smallest value for MaxSeqNum. You may assume that it suffices to find the smallest MaxSeqNum such that if DATA[MaxSeqNum] is in the receive window, then DATA[0] can no longer arrive. (b) Give an example showing that MaxSeqNum - 1 is not sufficient. (c) State a general rule for the minimum MaxSeqNum in terms of SWS and RWS. | Numerade
Suppose that we run the sliding window algorithm with SWS=5 and RWS =3, and no out-of-order arrivals. (a) Find the smallest value for MaxSeqNum. You may assume that it suffices to find the smallest MaxSeqNum such that if DATA[MaxSeqNum] is in the receive window, then DATA[0] can no longer arrive. (b) Give an example showing that MaxSeqNum - 1 is not sufficient. (c) State a general rule for the minimum MaxSeqNum in terms of SWS and RWS. | Numerade
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