8d36c6d00f
While the "Dynamic programming and recursion" section hosts this problem, the
optimal solution does not use recursion. Many cite the Fibonacci problem as a
quintessential dynamic programming question. I assume these people expect an
answer like:
```python
def fib(n):
cache = {0: 0, 1: 1}
def do_fib(n):
if n in cache:
return cache[n]
else:
cache[n - 1] = do_fib(n - 1)
cache[n - 2] = do_fib(n - 2)
return cache[n - 1] + cache[n - 2]
return do_fib(n)
```
The cache turns the runtime of the classic Fibonacci solution...
```python
def fib(n):
if n in {0, 1}:
return n
return fib(n - 1) + fib(n - 2)
```
... from O(2^n) to a O(n). But both the cache itself and the additional stacks
that the runtime allocates for each recursive call create an O(n) space
complexity.
InterviewCake wants the answer to be solved in O(n) time with O(1)
space. To achieve this, instead of solving fib(n) from the top-down, we solve it
from the bottom-up.
I found this problem to be satisfying to solve.
|
||
|---|---|---|
| .. | ||
| crack_the_coding_interview | ||
| data_structures_and_algorithms | ||
| deepmind | ||
| README.md | ||
Scratch
The purpose of the scratch directory is to host practice exercises. Practice
encompasses things like working on data structures and algorithms problems for
upcoming coding interviews or general aptitude as well as writing code snippets
to help me learn a new programming language or understand an unfamiliar concept.