1.Time Complexity
A indicator to evaluate the time cost of an algorithm.
1.1. O(1)
1.2. O(n)
1
2
| for i in range(n):
print(i)
|
1.3. O()
1
2
3
| for i in range(n):
for j in range(n):
print(i,j)
|
1.4. O(logn)
1
2
3
4
| n = 32
while n > 1:
n = n/2
print(n)
|
1.5. Common time complexity by order
2.Space Complexity
A indicator to evaluate the space cost of an algorithm.
2.1. O(1)
2.2. O(n)
2.3. O(nm)
1
2
| import numpy as np
np.random.rand(n, m)
|
3. Use more Space to save Time
As time is more expensive than memory, we should use memory to save time(if applicable).
- list: search, insert slower, less memory consuming
- dict: search, insert faster, more memory consuming