Lattice Paths
Starting in the top left corner of a grid, and only being able to move to the right and down, there are exactly 6 routes to the bottom right corner.
How many such routes are there through a grid?
Implementations
// Answer: 137846528820
#include <iostream>#include <vector>#include <cstdint>
uint64_t lattice_path(size_t grid_size){ std::vector< uint64_t > grid((grid_size+1)*(grid_size+1),1);
for (int x = grid_size-1; 0 <= x ; x--) { for (int y = grid_size-1; 0 <= y; y--) { int pos = (y*(grid_size+1))+x; grid.at(pos) = grid.at(pos+1) + grid.at(pos+(grid_size+1)); } } return grid.at(0);}
#if ! defined UNITTEST_MODEint main(int argc, char const *argv[]){ std::cout << "Answer: " << lattice_path(2) << std::endl; std::cout << "Answer: " << lattice_path(20) << std::endl; return 0;}#endif // #if ! defined UNITTEST_MODESolution Notes
Mathematical Background
This is a classic combinatorics problem. To navigate an n×n grid with only right and down moves, you need exactly n right moves and n down moves, for a total of 2n moves.
The number of paths is the number of ways to arrange n right moves and n down moves, which is given by the binomial coefficient:
For a 20×20 grid, this becomes , which is the number shown in the solutions.
Algorithm Analysis
Most implementations use direct binomial coefficient calculation rather than dynamic programming to avoid overflow and precision issues with large factorials.
- Direct factorial approach: Compute (2n)! / (n! * n!) but requires big integer arithmetic
- Iterative multiplication: Compute the coefficient by multiplying terms iteratively to avoid computing full factorials
- Dynamic programming: Build a grid where each cell represents paths to that point (shown in C++ and Ruby implementations)
Time complexity: O(n) for iterative binomial calculation, O(n²) for dynamic programming grid approach.
Performance Analysis
- Time Complexity: O(n) for iterative binomial coefficient calculation (n=20 multiplications), O(n²) for DP grid approach.
- Space Complexity: O(1) for iterative method, O(n²) for DP grid (400 cells for 20×20).
- Execution Time: Virtually instantaneous for both approaches, suitable for real-time computation.
- Scalability: Iterative method scales better for larger grids, DP approach becomes memory-intensive.
Key Insights
- The problem requires exactly n right and n down moves in some order
- Binomial coefficients grow extremely rapidly - C(40,20) is over 137 trillion
- Dynamic programming approach demonstrates the concept of optimal substructure
- The iterative binomial calculation is more memory-efficient than factorial computation
Educational Value
This problem teaches:
- Fundamental combinatorics and counting principles
- Binomial coefficients and their interpretation
- Dynamic programming for path counting problems
- The importance of choosing appropriate data types for large numbers
- How mathematical insight can replace brute-force enumeration
Key Insights
- The problem requires exactly n right and n down moves in some order
- Binomial coefficients grow extremely rapidly - C(40,20) is over 137 trillion
- Dynamic programming approach demonstrates the concept of optimal substructure
- The iterative binomial calculation is more memory-efficient than factorial computation
Educational Value
This problem teaches:
- Fundamental combinatorics and counting principles
- Binomial coefficients and their interpretation
- Dynamic programming for path counting problems
- The importance of choosing appropriate data types for large numbers
- How mathematical insight can replace brute-force enumeration