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"""

This module provides two implementations for the rod-cutting problem:

1. A naive recursive implementation which has an exponential runtime

2. Two dynamic programming implementations which have quadratic runtime

The rod-cutting problem is the problem of finding the maximum possible revenue

obtainable from a rod of length ``n`` given a list of prices for each integral piece

of the rod. The maximum revenue can thus be obtained by cutting the rod and selling the

pieces separately or not cutting it at all if the price of it is the maximum obtainable.

"""

def naive_cut_rod_recursive(n: int, prices: list):

"""

Solves the rod-cutting problem via naively without using the benefit of dynamic

programming. The results is the same sub-problems are solved several times

leading to an exponential runtime

Runtime: O(2^n)

Arguments

-------

n: int, the length of the rod

prices: list, the prices for each piece of rod. ``p[i-i]`` is the

price for a rod of length ``i``

Returns

-------

The maximum revenue obtainable for a rod of length n given the list of prices

for each piece.

Examples

--------

>>> naive_cut_rod_recursive(4, [1, 5, 8, 9])

>>> naive_cut_rod_recursive(10, [1, 5, 8, 9, 10, 17, 17, 20, 24, 30])

"""

_enforce_args(n, prices)

if n == 0:

return 0

max_revue = float("-inf")

for i in range(1, n + 1):

max_revue = max(

max_revue, prices[i - 1] + naive_cut_rod_recursive(n - i, prices)

)

return max_revue

def top_down_cut_rod(n: int, prices: list):

"""

Constructs a top-down dynamic programming solution for the rod-cutting

problem via memoization. This function serves as a wrapper for

_top_down_cut_rod_recursive

Runtime: O(n^2)

Arguments

--------

n: int, the length of the rod

prices: list, the prices for each piece of rod. ``p[i-i]`` is the

price for a rod of length ``i``

Note

----

For convenience and because Python's lists using 0-indexing, length(max_rev) =

n + 1, to accommodate for the revenue obtainable from a rod of length 0.

Returns

-------

The maximum revenue obtainable for a rod of length n given the list of prices

for each piece.

Examples

-------

>>> top_down_cut_rod(4, [1, 5, 8, 9])

>>> top_down_cut_rod(10, [1, 5, 8, 9, 10, 17, 17, 20, 24, 30])

"""

_enforce_args(n, prices)

max_rev = [float("-inf") for _ in range(n + 1)]

return _top_down_cut_rod_recursive(n, prices, max_rev)

def _top_down_cut_rod_recursive(n: int, prices: list, max_rev: list):

"""

Constructs a top-down dynamic programming solution for the rod-cutting problem

via memoization.

Runtime: O(n^2)

Arguments

--------

n: int, the length of the rod

prices: list, the prices for each piece of rod. ``p[i-i]`` is the

price for a rod of length ``i``

max_rev: list, the computed maximum revenue for a piece of rod.

``max_rev[i]`` is the maximum revenue obtainable for a rod of length ``i``

Returns

-------

The maximum revenue obtainable for a rod of length n given the list of prices

for each piece.

"""

if max_rev[n] >= 0:

return max_rev[n]

elif n == 0:

return 0

else:

max_revenue = float("-inf")

for i in range(1, n + 1):

max_revenue = max(

max_revenue,

prices[i - 1] + _top_down_cut_rod_recursive(n - i, prices, max_rev),

)

max_rev[n] = max_revenue

return max_rev[n]

def bottom_up_cut_rod(n: int, prices: list):

"""

Constructs a bottom-up dynamic programming solution for the rod-cutting problem

Runtime: O(n^2)

Arguments

----------

n: int, the maximum length of the rod.

prices: list, the prices for each piece of rod. ``p[i-i]`` is the

price for a rod of length ``i``

Returns

-------

The maximum revenue obtainable from cutting a rod of length n given

the prices for each piece of rod p.

Examples

-------

>>> bottom_up_cut_rod(4, [1, 5, 8, 9])

>>> bottom_up_cut_rod(10, [1, 5, 8, 9, 10, 17, 17, 20, 24, 30])

"""

_enforce_args(n, prices)

# length(max_rev) = n + 1, to accommodate for the revenue obtainable from a rod of

# length 0.

max_rev = [float("-inf") for _ in range(n + 1)]

max_rev[0] = 0

for i in range(1, n + 1):

max_revenue_i = max_rev[i]

for j in range(1, i + 1):

max_revenue_i = max(max_revenue_i, prices[j - 1] + max_rev[i - j])

max_rev[i] = max_revenue_i

return max_rev[n]

def _enforce_args(n: int, prices: list):

"""

Basic checks on the arguments to the rod-cutting algorithms

n: int, the length of the rod

prices: list, the price list for each piece of rod.

Throws ValueError:

if n is negative or there are fewer items in the price list than the length of

the rod

"""

if n < 0:

raise ValueError(f"n must be greater than or equal to 0. Got n = {n}")

if n > len(prices):

raise ValueError(

f"Each integral piece of rod must have a corresponding "

f"price. Got n = {n} but length of prices = {len(prices)}"

)

def main():

prices = [6, 10, 12, 15, 20, 23]

n = len(prices)

# the best revenue comes from cutting the rod into 6 pieces, each

# of length 1 resulting in a revenue of 6 * 6 = 36.

expected_max_revenue = 36

max_rev_top_down = top_down_cut_rod(n, prices)

max_rev_bottom_up = bottom_up_cut_rod(n, prices)

max_rev_naive = naive_cut_rod_recursive(n, prices)

assert expected_max_revenue == max_rev_top_down

assert max_rev_top_down == max_rev_bottom_up

assert max_rev_bottom_up == max_rev_naive

if __name__ == "__main__":

main()

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