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1. The following code recursively calculates the maximum value in a list. def recursive_max_impl( L, i): The actual recursive function. if i == len(L)-1: return L[i] else: return max(L[i], recursive_max_impl(L,i+1 def recursive_max (L): The driver for the recursive function. This case of an empty list and otherwise makes the recursive function. if len(L) == 0: return -99999 # By convention else: return recursive_max_impl(L, 0) if name == "main": L1 = [5] print(recursive_max (L1)) L2 = [ 24, 23.1, 12, 15, 1] print(recursive_max(L2)) L2.append(55) print(recursive_max(L2)) Using this as a guide, write a recursive function to add the values in a list. You should have to change very little code. Implement your code in the provided file prob1.py 2. Implement a recursive solution to the Fibonacci number function definition given during lecture. Implement your code in the provided file prob2.py. 3. Looking carefully at the Fibonacci definition shows that calculating Fibonacci number $f_{n-1}$ requires calculating Fibonacci number $f_{n-2}$, which is also required for calculating Fibonacci number $f_n$. This means there is redundant computation. This redundancy gets worse for numbers $f_{n-3}$, $f_{n-4}$, etc. Fortunately, the Fibonacci sequence is relatively

          1. The following code recursively calculates the
maximum value in a list.
def recursive_max_impl( L, i):
The actual recursive function.
if i == len(L)-1:
return L[i]
else:
return max(L[i], recursive_max_impl(L,i+1
def recursive_max (L):
The driver for the recursive function. This
case of an empty list and otherwise makes the
recursive function.
if len(L) == 0:
return -99999 # By convention
else:
return recursive_max_impl(L, 0)
if __name__ == "__main__":
L1 = [5]
print(recursive_max (L1))
L2 = [ 24, 23.1, 12, 15, 1]
print(recursive_max(L2))
L2.append(55)
print(recursive_max(L2))
Using this as a guide, write a recursive function to
add the values in a list. You should have to change
very little code. Implement your code in the
provided file prob1.py
2. Implement a recursive solution to the Fibonacci
number function definition given during lecture.
Implement your code in the provided file
prob2.py.
3. Looking carefully at the Fibonacci definition
shows that calculating Fibonacci number $f_{n-1}$
requires calculating Fibonacci number $f_{n-2}$, which
is also required for calculating Fibonacci number
$f_n$. This means there is redundant computation.
This redundancy gets worse for numbers $f_{n-3}$,
$f_{n-4}$, etc.
Fortunately, the Fibonacci sequence is relatively

1. The following code recursively calculates the
maximum value in a list.
def recursivemaximpl( L, i):
The actual recursive function.
if i == len(L)-1:
return L[i]
else:
return max(L[i], recursivemaximpl(L,i+1
def recursivemax (L):
The driver for the recursive function. This
case of an empty list and otherwise makes the
recursive function.
if len(L) == 0:
return -99999 # By convention
else:
return recursivemaximpl(L, 0)
if name == "main":
L1 = [5]
print(recursivemax (L1))
L2 = [ 24, 23.1, 12, 15, 1]
print(recursivemax(L2))
L2.append(55)
print(recursivemax(L2))
Using this as a guide, write a recursive function to
add the values in a list. You should have to change
very little code. Implement your code in the
provided file prob1.py
2. Implement a recursive solution to the Fibonacci
number function definition given during lecture.
Implement your code in the provided file
prob2.py.
3. Looking carefully at the Fibonacci definition
shows that calculating Fibonacci number fn-1
requires calculating Fibonacci number fn-2, which
is also required for calculating Fibonacci number
fn. This means there is redundant computation.
This redundancy gets worse for numbers fn-3,
fn-4, etc.
Fortunately, the Fibonacci sequence is relatively

Added by Tammy R.

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