mirror of
https://github.com/krahets/hello-algo.git
synced 2024-12-25 14:16:29 +08:00
translation: Update replace_linear_by_hashing.md
refined some parts of it.
This commit is contained in:
parent
6a74972080
commit
3e40c1153d
1 changed files with 5 additions and 5 deletions
|
@ -1,6 +1,6 @@
|
||||||
# Hash optimization strategies
|
# Hash optimization strategies
|
||||||
|
|
||||||
In algorithm problems, **we often reduce the time complexity of algorithms by replacing linear search with hash search**. Let's use an algorithm problem to deepen understanding.
|
In algorithm problems, **we often reduce an algorithm's time complexity by replacing a linear search with a hash-based search**. Let's use an algorithm problem to deepen the understanding.
|
||||||
|
|
||||||
!!! question
|
!!! question
|
||||||
|
|
||||||
|
@ -8,7 +8,7 @@ In algorithm problems, **we often reduce the time complexity of algorithms by re
|
||||||
|
|
||||||
## Linear search: trading time for space
|
## Linear search: trading time for space
|
||||||
|
|
||||||
Consider traversing all possible combinations directly. As shown in the figure below, we initiate a two-layer loop, and in each round, we determine whether the sum of the two integers equals `target`. If so, we return their indices.
|
Consider traversing through all possible combinations directly. As shown in the figure below, we initiate a nested loop, and in each iteration, we determine whether the sum of the two integers equals `target`. If so, we return their indices.
|
||||||
|
|
||||||
![Linear search solution for two-sum problem](replace_linear_by_hashing.assets/two_sum_brute_force.png)
|
![Linear search solution for two-sum problem](replace_linear_by_hashing.assets/two_sum_brute_force.png)
|
||||||
|
|
||||||
|
@ -18,11 +18,11 @@ The code is shown below:
|
||||||
[file]{two_sum}-[class]{}-[func]{two_sum_brute_force}
|
[file]{two_sum}-[class]{}-[func]{two_sum_brute_force}
|
||||||
```
|
```
|
||||||
|
|
||||||
This method has a time complexity of $O(n^2)$ and a space complexity of $O(1)$, which is very time-consuming with large data volumes.
|
This method has a time complexity of $O(n^2)$ and a space complexity of $O(1)$, which becomes very time-consuming with large data volumes.
|
||||||
|
|
||||||
## Hash search: trading space for time
|
## Hash search: trading space for time
|
||||||
|
|
||||||
Consider using a hash table, with key-value pairs being the array elements and their indices, respectively. Loop through the array, performing the steps shown in the figure below each round.
|
Consider using a hash table, where the key-value pairs are the array elements and their indices, respectively. Loop through the array, performing the steps shown in the figure below during each iteration.
|
||||||
|
|
||||||
1. Check if the number `target - nums[i]` is in the hash table. If so, directly return the indices of these two elements.
|
1. Check if the number `target - nums[i]` is in the hash table. If so, directly return the indices of these two elements.
|
||||||
2. Add the key-value pair `nums[i]` and index `i` to the hash table.
|
2. Add the key-value pair `nums[i]` and index `i` to the hash table.
|
||||||
|
@ -42,6 +42,6 @@ The implementation code is shown below, requiring only a single loop:
|
||||||
[file]{two_sum}-[class]{}-[func]{two_sum_hash_table}
|
[file]{two_sum}-[class]{}-[func]{two_sum_hash_table}
|
||||||
```
|
```
|
||||||
|
|
||||||
This method reduces the time complexity from $O(n^2)$ to $O(n)$ by using hash search, greatly improving the running efficiency.
|
This method reduces the time complexity from $O(n^2)$ to $O(n)$ by using hash search, significantly improving the running efficiency.
|
||||||
|
|
||||||
As it requires maintaining an additional hash table, the space complexity is $O(n)$. **Nevertheless, this method has a more balanced time-space efficiency overall, making it the optimal solution for this problem**.
|
As it requires maintaining an additional hash table, the space complexity is $O(n)$. **Nevertheless, this method has a more balanced time-space efficiency overall, making it the optimal solution for this problem**.
|
||||||
|
|
Loading…
Reference in a new issue