Approach
Dynamic programming
For Palindrome Partitioning IV, the implementation records answers for smaller states and reuses them to build the requested result without repeating work.
- Define precisely what one DP state represents.
- Establish the base cases before transitions are evaluated.
- Process states in dependency order and combine only already-known values.
Code notes
- 18 lines of Python from the credited upstream file 1745.py.
- The implementation keeps its working state in language-native values and containers.
- No explicit loop blocks detected.
Complexity
Multiply the number of reachable states by the work performed for each transition, then include the stored state table in memory usage.
Check the problem constraints before deciding whether this complexity will pass.