1. The document discusses time and space complexity analysis of algorithms. It analyzes three functions - abc(), Sum(), and RSum(). 2. For abc(), the time complexity is O(1) as it performs a constant number of operations. For Sum(), the time complexity is O(n) as it iterates through an array of size n. 3. RSum() recursively sums an array. Its time and space complexity is O(3n) and O(n) respectively, as the depth of recursion is n and at each level it uses constant additional space.

1. [팀 이름] 소집단 활동 보고서, 과제수행일지
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4. (complexity)
(complexity) (space
complexity) (time complexity) .
4.1 (complexity)
4.2 (space complexity)
4.3 (time complexity)
4.1 (complexity)
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[ 4.1] (complexity)
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2. • (space complexity):
4.2 (space complexity)
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[ 4.1] 1
float abc(float a, float b, float c)
{
return(a + b + b*c + (a + b - c)/(a + b) + 4.0);
}
= 0
a, b, c . , float
(word) , '3 ' .
a, b, c (parameter) abc
0 .
[ 4.2] 2
float Sum(float a[], int n)
{
float s = 0.0;

3. for(int i = 1; i < = n; i++)
s += a[i];
return s;
}
= n + 3
a[], n, s, i . a[] n
. [ 4.1] a[] n
. , n for- .
a[] n .
(a[] ) + ( n, s, I ) = n + 3 .
[ 4.3] 3
float RSum(float a[], int n)
{
if(n <= 0)
return (0.0);
else
return (RSum(a, n-1) + a[n]);
}
= 3(n + 1)
(resursion) . a[], n .
n if- . , a[]
a[] n .
a[] n , (a[] n
) + (n ) = 1 + 1 .
. ,
(the depth of recursion) ,
(return address) .
(depth of recursion)×(a[n], n, ) = (n+1)×3 .