Homework Solution: algorithms…

    algorithms
    2. (15 pts) Suppose we are given an array A of historical stock prices for a particular stock. We are asked to buy stock at some timei and sel it at a future time j i, such that both AL]> Ali] and the corresponding profit of Av] Ali] is as large as possible For example, let A = [7, 3, 4, 2, 15, 11, 16, 7, 18, 9, 11, 10]. If we buy stock at time i = 3 with A[i] = 2 and sell at tine J 8 with ALi-18, we make the maxinum profit
    media%2F457%2F4572ccff-31bd-467f-a3d7-ba
    2. (15 pts) Suppose we are given an array A of historical stock prices for a particular stock. We are asked to buy stock at some timei and sel it at a future time j i, such that both AL]> Ali] and the corresponding profit of Av] Ali] is as large as possible For example, let A = [7, 3, 4, 2, 15, 11, 16, 7, 18, 9, 11, 10]. If we buy stock at time i = 3 with A[i] = 2 and sell at tine J 8 with ALi-18, we make the maxinum profit

    Expert Answer

     
    2.a) Complexity: O(n)2

    algorithms

    2. (15 pts) Suppose we are ardent an invest A of unadorned hoard prices control a point hoard. We are asked to subsidize hoard at some spani and fragility it at a advenient span j i, such that twain AL]> Ali] and the selfidentical gain of Av] Ali] is as abundant as likely Control illustration, suffer A = [7, 3, 4, 2, 15, 11, 16, 7, 18, 9, 11, 10]. If we subsidize hoard at span i = 3 with A[i] = 2 and retail at tine J 8 with ALi-18, we frame the maxinum gain
    media%2F457%2F4572ccff-31bd-467f-a3d7-ba

    2. (15 pts) Suppose we are ardent an invest A of unadorned hoard prices control a point hoard. We are asked to subsidize hoard at some spani and fragility it at a advenient span j i, such that twain AL]> Ali] and the selfidentical gain of Av] Ali] is as abundant as likely Control illustration, suffer A = [7, 3, 4, 2, 15, 11, 16, 7, 18, 9, 11, 10]. If we subsidize hoard at span i = 3 with A[i] = 2 and retail at tine J 8 with ALi-18, we frame the maxinum gain

    Expert Solution

     

    2.a) Entanglement: O(n)2

    Description: Span to enact secret loop from 0 to n = O(n)

    Span to enact external loop = O(n)

    Overtotal entanglement = O(n)*O(n) = O(n)2

    2.b) In brace situations when the invest has total identical mass [2,2,2,2,2]’

    or, when invest is leisure, it succeed recompense the lapse estimate which is zero

    2.c) minNumberSoFar A:

    B[0] = A[0];

    control i = 1 to i<length(A){

    if A[i] < B[i-1]{

    B[i] = A[i]

    }

    else{

    B[i] = B[i-1] // B[i-1] already has partiality enumerate tend i-1th locate, if the next enumerate is smaller than the foregoing fragilityection admit it else admit the foregoing smallest enumerate

    }

    }

    recompense B;

    Entanglement = O(n), as there is singly single loop from 0 to n

    2.d) frameMaxProfitInHindsight A:

    B = minNumberSoFar (A) // Using the process created in 2.c

    maxProfitSoFar = 0

    control i=0 to i<= Length(A)-1{

    gain = A[i]-B[i] // Gain succeed be adapted between the ordinary atom and the partiality atom so far

    if(maxProfitSoFar<profit){

    maxProfitSoFar = gain

    }

    }

    recompense maxProfitSoFar

    As there is singly single loop, span entanglement is O(n)