Traveling salesman #229
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Traveling salesman #229
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Pull Request Overview
This PR adds a new implementation of the Traveling Salesman Problem (TSP) solver using Dynamic Programming with bitmasking. The solution efficiently computes the minimum cost to visit all cities exactly once and return to the starting city using memoization.
Key Changes:
- Implements TSP with DP and bitmasking technique for optimal path finding
- Uses recursive approach with memoization to avoid recomputation
- Includes a working example with a 4-city cost matrix
| # Try visiting all unvisited cities | ||
| for (city in 0:(n - 1)) { | ||
| if ((mask & (1 << city)) == 0) { | ||
| newAns <- cost[pos + 1, city + 1] + tsp(mask | (1 << city), city, cost, dp, n) |
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The variable name 'newAns' uses camelCase, which is inconsistent with R naming conventions. According to the repository guidelines, variables should use '.' or '_' to separate words (e.g., 'new_ans' or 'new.ans').
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It's already implemented here |
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This program solves the Traveling Salesman Problem (TSP) using Dynamic Programming with Bitmasking.
It finds the minimum cost path to visit all cities exactly once and return to the starting city.
Uses a recursive function tsp(mask, pos) where mask tracks visited cities (in binary) and pos is the current city.
The DP table (dp) stores already computed states to avoid recomputation.
Base case: when all cities are visited, return cost to return to start.
Time Complexity: O(n² * 2ⁿ)
Output: Minimum traveling cost.