λμ νλ‘κ·Έλλ°Intermediate
μ΅μ₯ μ¦κ° λΆλΆ μμ΄
λͺ¨λ μμκ° μ¦κ°νλ μμμΈ κ°μ₯ κΈ΄ λΆλΆ μμ΄μ μ°Ύμ΅λλ€. λνμ μΈ λμ νλ‘κ·Έλλ° λ¬Έμ μ λλ€.
#dynamic-programming#binary-search#optimization#sequences
Complexity Analysis
Time (Average)
O(n log n)Expected case performance
Space
O(n)Memory requirements
Time (Best)
O(n log n)Best case performance
Time (Worst)
O(n log n)Worst case performance
How it works
- β’ Find longest subsequence in increasing order
- β’ Dynamic programming approach
- β’ O(nΒ²) time complexity (DP solution)
- β’ O(n) space complexity
- β’ Classic DP problem with optimal substructure
Step: 1 / 0
500ms
SlowFast
Keyboard Shortcuts
Space Play/Pauseβ β StepR Reset1-4 Speed
Real-time Statistics
Algorithm Performance Metrics
Progress0%
Comparisons
0
Swaps
0
Array Accesses
0
Steps
1/ 0
Algorithm Visualization
Step 1 of 0
Initialize array to begin
Default
Comparing
Swapped
Sorted
Code Execution
Currently executing
Previously executed
Implementation