During tough times like these, investment becomes more uncertain with more dangers. To solve it, we might try to train a model to decide when to buy or se Therefore, to provide it with correct data, we plan to design an algorithm that answers: • what is the perfect moment to buy and when to sell to maximize your profit? Assume you must buy Input: changes : array listing changes in the prices, where indices represent days; it has at least two values Output i : index of the change before which we buy j: index of the change before which we sell maxProfit : the profit of this interval Example: Assume the below table contains the prices of a particular stock over days prices changes Day Value 50 1 63 70 40 4 55 65 60 7 72 79 68 10 74
During tough times like these, investment becomes more uncertain with more dangers. To solve it, we might try to train a model to decide when to buy or se Therefore, to provide it with correct data, we plan to design an algorithm that answers: • what is the perfect moment to buy and when to sell to maximize your profit? Assume you must buy Input: changes : array listing changes in the prices, where indices represent days; it has at least two values Output i : index of the change before which we buy j: index of the change before which we sell maxProfit : the profit of this interval Example: Assume the below table contains the prices of a particular stock over days prices changes Day Value 50 1 63 70 40 4 55 65 60 7 72 79 68 10 74
Computer Networking: A Top-Down Approach (7th Edition)
7th Edition
ISBN:9780133594140
Author:James Kurose, Keith Ross
Publisher:James Kurose, Keith Ross
Chapter1: Computer Networks And The Internet
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![During tough times like these, investment becomes more uncertain with more dangers. To solve it, we might try to train a model to decide when to buy or sell.
Therefore, to provide it with correct data, we plan to design an algorithm that answers:
what is the perfect moment to buy and when to sell to maximize your profit?
Assume you must buy
Input:
changes : array listing changes in the prices, where indices represent days; it has at least two values
Output:
i : index of the change before which we buy
j: index of the change before which we sell
maxProfit : the profit of this interval
Example:
Assume the below table contains the prices of a particular stock over days
prices changes
Day Value
50
1
63
13
70
7
3
40
-30
55
15
65
10
6
60
-5
7
72
12
8
79
7
9
68
-11
10
74
6
Therefore, the output of maxProfit([13,7,-30,15,10, -5,12,7, -11,6]) should be (3, 7, 39) . This is because our maximum profit would be 39
when we buy the stock at day 4, index of 3, and sell after day 8, index of 7. Then, the total profit is 15+ 10 – 5+ 12 +7 = 39
We will try to solve the problem using various techniques:
Brute-Force
Divide-and-Conquer](/v2/_next/image?url=https%3A%2F%2Fcontent.bartleby.com%2Fqna-images%2Fquestion%2F3dfe85b1-e5f0-4a73-ab4f-8ab481027dba%2F13475e5a-4447-4e0d-b82d-f57ec9533c62%2F2b4ejx_processed.png&w=3840&q=75)
Transcribed Image Text:During tough times like these, investment becomes more uncertain with more dangers. To solve it, we might try to train a model to decide when to buy or sell.
Therefore, to provide it with correct data, we plan to design an algorithm that answers:
what is the perfect moment to buy and when to sell to maximize your profit?
Assume you must buy
Input:
changes : array listing changes in the prices, where indices represent days; it has at least two values
Output:
i : index of the change before which we buy
j: index of the change before which we sell
maxProfit : the profit of this interval
Example:
Assume the below table contains the prices of a particular stock over days
prices changes
Day Value
50
1
63
13
70
7
3
40
-30
55
15
65
10
6
60
-5
7
72
12
8
79
7
9
68
-11
10
74
6
Therefore, the output of maxProfit([13,7,-30,15,10, -5,12,7, -11,6]) should be (3, 7, 39) . This is because our maximum profit would be 39
when we buy the stock at day 4, index of 3, and sell after day 8, index of 7. Then, the total profit is 15+ 10 – 5+ 12 +7 = 39
We will try to solve the problem using various techniques:
Brute-Force
Divide-and-Conquer
![Divide and Conquer
Now, we would try to use a divide and conquer paradigm. Hint: try the three steps, and design multiple versions to master these steps
In [ ]: # write your implementation here
def maxProfit(changes):
This is just an interface for <maxProfitDivide>
it returns the indices of (i,j) indicating the day to buy and sell respectively
to have the maximum profit in a list of prices per day in <changes>.
Inputs:
- changes: the list holding the changes in prices; the value whose index is k represents
the change between day <k> and day <k+1>
<changes> has at least a single change [two days]
Output:
- i: the index of the change before which we buy
- j: the index of the change after which we sell
- maxProfit: the value of the maximum profit
return (0,0,0)
In [ ]: maxProfit([13,7,-30,15,10,-5,12,7,-11,6])](/v2/_next/image?url=https%3A%2F%2Fcontent.bartleby.com%2Fqna-images%2Fquestion%2F3dfe85b1-e5f0-4a73-ab4f-8ab481027dba%2F13475e5a-4447-4e0d-b82d-f57ec9533c62%2Fm7pon0b_processed.png&w=3840&q=75)
Transcribed Image Text:Divide and Conquer
Now, we would try to use a divide and conquer paradigm. Hint: try the three steps, and design multiple versions to master these steps
In [ ]: # write your implementation here
def maxProfit(changes):
This is just an interface for <maxProfitDivide>
it returns the indices of (i,j) indicating the day to buy and sell respectively
to have the maximum profit in a list of prices per day in <changes>.
Inputs:
- changes: the list holding the changes in prices; the value whose index is k represents
the change between day <k> and day <k+1>
<changes> has at least a single change [two days]
Output:
- i: the index of the change before which we buy
- j: the index of the change after which we sell
- maxProfit: the value of the maximum profit
return (0,0,0)
In [ ]: maxProfit([13,7,-30,15,10,-5,12,7,-11,6])
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