Answer: True
Explanation:
Subset sum problem and Knapsack problem can be solved using dynamic programming.
In case of Knapsack problem there is a set of weights associative with objects and a set of profits associated with each object and a total capacity of knapsack let say C. With the help of dynamic programming we try to include object's weight such that total profit is maximized without fragmenting any weight of objects and without exceeding the capacity of knapsack, it is also called as 0/1 knapsack problem.
Similar to knapsack problem, in subset sum problem there is set of items and a set of weights associated with the items and a capacity let say C, task is to choose the subset of items such that total sum of weights associated with items of subset is maximized without exceeding the total capacity.
On the basis of above statements we can say that subset sum problem is generalization of knapsack problem.
Answer:
A. The parent-teacher orginization keeps a log of cookies sales to raise money for the elementary school.
Explanation:
Answer:
Not all people but sometimes it happens
Answer:
5) 3 0 0
Explanation:
Given data
int [] val = { 3, 10, 44 };
The total number of parameters of given array are 3, so total length of array is also 3.
The indexing of array starts with '0', Therefore the indexes of array with length zero are: {0,1,2}
The value of array at index 0 is = 3
similarly
value at index 1 = 10
value at index 2 = 44
Here, Int i = 1 is storing the value '1' in integer variable i.
In addition to that, any value of index 'i' of an array is selected using array[i].
Therefore,
val[i] = i-1 is copying the value (i-1 = 1-1 = 0) to the index '1' of the array because i = 1.
So value at index 1 would be = val[1] = 0
The term i++ is incrementing the value of i, it makes i =2
val[i] = i-1 is copying the value (i-1 = 1-1 = 0) to the index '2' of the array because i = 2 now.
So value at index 2 would be = val[2] = 0
Hence, the output would be {3 0 0}. So 5th option is correct.
