Given an un-directed fully connected graph (there is an edge between every two vertices) with a weight function that has the triangle inequality. I.e., if (u,v), (v,w), (u,w) in E then w(u,v) + w(v,w) >= w(u,w). Do:
Ratio from the optimum:
Note that the optimum (opt) path costs at most the value of the MST (just take one edge off the opt and you get a spanning tree). Since our path is at most twice the cost of the spanning tree we have a ratio of x2.
Yes, the traveling salesman problem is an example of a co-NP-complete problem.
Some effective heuristics for solving the traveling salesman problem efficiently include the nearest neighbor algorithm, the genetic algorithm, and the simulated annealing algorithm. These methods help to find approximate solutions by making educated guesses and refining them iteratively.
Robert W. Starr has written: 'A multi-tour heuristic for the traveling salesman problem' -- subject(s): Traveling-salesman problem
Some alternative solutions to the Traveling Salesman Problem (TSP) include genetic algorithms, ant colony optimization, simulated annealing, and branch and bound algorithms.
There are several free programs available for this sort of problem
The traveling salesman problem can be efficiently solved using dynamic programming by breaking down the problem into smaller subproblems and storing the solutions to these subproblems in a table. This allows for the reuse of previously calculated solutions, reducing the overall computational complexity and improving efficiency in finding the optimal route for the salesman to visit all cities exactly once and return to the starting point.
The 2-approximation algorithm for the Traveling Salesman Problem is a method that provides a solution that is at most twice the optimal solution. This algorithm works by finding a minimum spanning tree of the given graph and then traversing the tree to form a tour that visits each vertex exactly once.
The Fable of the Traveling Salesman - 1923 was released on: USA: 11 March 1923
The Traveling Salesman Problem (TSP) is significant in Operations Research as it involves finding the most efficient route for a salesman to visit multiple locations. In the context of the Production Function (PF), solving the TSP can optimize logistics and reduce costs in delivering goods or services, improving overall efficiency in production processes.
The best strategies for solving the Traveling Salesman Problem with Profit Function (TSP-PF) involve using optimization algorithms such as genetic algorithms, ant colony optimization, or simulated annealing. These algorithms help find the most efficient route for the salesman to visit all locations while maximizing profit. Additionally, incorporating heuristics and problem-specific constraints can further improve the solution quality.
traveling salesman
Traveling Salesman - 1921 was released on: USA: 5 June 1921 Finland: 17 February 1924